Cognitive health assessment for core cognitive functions

ABSTRACT

An improved system for assessing cognitive function is described that uses tracked electrical activity of the brain of the individuals in response to a specific sequence of stimuli in generating data sets, which, for example, can be encapsulated as a data structure. The data sets can include tracked specific response types, at different times and amplitudes, including, but not limited to, event related potential signal components. Brainwave features including, event related potentials, are tracked in relation to both pre-attentive brain responses and consciously controlled attention responses.

CROSS REFERENCE

This application is a Continuation of U.S. application Ser. No.17/975,673, filed Oct. 28, 2022, which is a Continuation of U.S.application Ser. No. 17/587,646, filed Jan. 28, 2022, which is aContinuation of U.S. application Ser. No. 16/513,469 filed on Jul. 16,2019, now U.S. Pat. No. 11,278,230, which is a non-provisional of, andclaims all benefit, including priority to, U.S. application Ser. No.62/698,424, entitled “SYSTEMS AND METHODS FOR COGNITIVE HEALTHASSESSMENT”, filed on Jul. 16, 2018, incorporated herein by reference inits entirety.

FIELD

Embodiments described herein relate to systems, methods, andcomputer-readable media for assessing cognitive function in concussionsand acquired brain injuries. More specifically, embodiments relate tocomputer-implemented devices, and apparatuses that are configured toexecute, facilitate or support cognitive health assessments usingtechnical approaches for monitoring electrical activity of the brain ofan individual.

INTRODUCTION

Cognitive health assessments are useful in rehabilitation, diagnostics,prognostics, and assessing severity of neurocognitive issues that may,for example, arise following a concussion or an acquired brain injury.

Cognitive health assessments are difficult as cognitive function ischallenging to measure. The most relevant metric for assessing anddiagnosing the consequences of a concussion and acquired brain injury iscognitive function—how a brain responds to stimuli.

Increased challenges arise in relation to individuals who appear to beunresponsive (e.g., those who have severe brain damage), and are unableto provide meaningful responses to stimuli.

Where individuals are concussed or potentially concussed, there may beincreased worry associated with a visit to a practitioner. An additionalchallenge is that the visit to the practitioner skews readings onsubjective examinations, such as White Coat Syndrome.

SUMMARY

Improved approaches are described herein in relation to cognitivefunction to determine brain health, specifically applicable toconcussion, comatose/unresponsive individuals, and acquired braininjury. Further applications include mental competency assessments,elderly competency assessments, neurodevelopmental disorder competencyassessments, measuring drug effects on brain function, and general brainhealth tracking. Some approaches described herein are especially helpfulfor seemingly unresponsive individuals as indicators may establish thatsuch individuals may potentially have cognitive function that was notrecognized otherwise.

An improved system for assessing cognitive function is described thatuses tracked electrical activity of the brain of the individuals inresponse to a specific sequence of stimuli in generating data sets,which, for example, can be encapsulated as a data structure. The datasets can include tracked specific response types, at different times andamplitudes, including, but not limited to, event related potential (ERP)signal components. Brainwave features including ERPs are tracked inrelation to both pre-attentive brain responses and consciouslycontrolled attention responses. The approach uses tracked electricalactivity of the brain of the individuals (e.g., during rest or inresponse to a specific sequence of stimuli) in generating data sets,which, for example, can be encapsulated as a data structure, obtainedfrom a sensory device coupled (e.g., attached) to the head of anindividual. The data sets can include tracked specific response types,at different times and amplitudes, tracking signal strengths andlatencies.

A computing device is utilized to generate, present (e.g., render on ascreen, control actuation of a scent mechanism, play a sound on aspeaker) a sequence of stimuli and the data sets are retrieved inaccordance with both rest and event-related potentials as the sequenceof stimuli are controlled to be presented by the computing device.Stimuli can include regular stimuli, and generated stimuli that isspecifically adapted to be dissonant, incongruous, unexpected,surprising, or deviant (e.g., 2400 tones can be selected, for example,with 82% standard tones (at around 50 ms, 1000 Hz, 80 dB), and threetypes of deviant tones at 6% of the population each). Deviants, forexample, can include duration deviants (e.g., 125 ms), frequencydeviants (e.g., 1200 Hz), and intensity deviants (e.g., 90 dB SPL).

The generated stimuli and the regular stimuli are presented such thatthe order in which they are presented and the tracking thereof ofelectrical signals in the brains are relevant, in some embodiments.

The tracking of different electrical signals may location-based, in someembodiments for example, dividing the scalp into multiple regions ofinterest with statistical analyses being conducted in different scalpsectors, tracking electrical signal amplitude and latency. Specificevent potentials are tracked in response to specific types of rest,stimuli, and modified stimuli, in accordance with a process forcognitive health assessments. The specific event potentials can includewindows for detecting peak amplitudes.

As described in various embodiments, specific improved processes areused in combination with technical processing circuitry and sensors togenerate data outputs that can be used to control graphical userinterfaces, append information into health record data structures, ormodify the functioning of medical devices (e.g., modifying thresholds orpolling frequencies). The processes are useful in respect of providing atechnical approach for investigating individuals who may have latentconcussion symptoms that are otherwise hard to detect, or individualswho appear to be vegetative, comatose, or otherwise unresponsive.

The data sets can be used for generating data outputs that can beprovided to downstream medical devices or systems for generatingnotifications, modifying treatment parameters (e.g., such as a pollingrate or modifying notification thresholds), among others. In someembodiments, the outputs are utilized to update electronic healthrecords, such as incorporating cognitive health assessment data into apatient's records so that it can be retrieved by a primary carepractitioner.

Where electronic health records are appended with cognitive assessmentdetails, medical devices can be automatically operated with modifiedparameters to, for example, increase machine vigilance of the individual(e.g., this comatose person may actually be responsive). Similarly,health records for individuals who are potentially concussed may beautomatically processed to cause the population of additional follow uptests and diagnostics.

For example, a comatose or unresponsive patient may have a flag variabletoggled such that the primary care practitioner knows that the dataindicates that the individual may actually have cognitive function.Similarly, for a potentially concussed patient, the flag variable couldindicate that the patient likely requires additional monitoring ortreatment intervention.

The cognitive health assessment data, in some embodiments, istransformed into inputs that can be used to generate graphical userinterfaces that include information that is adapted for display on adisplay screen or on a mobile device, for example, with interactivevisual elements. A graphical user interface can include visualinteractive or visual interface elements/controls which show electricalsignal components tracked against population-level details, which can bechanged from numerical representations into percentile value scalesbased against, demographically selected control groups. These percentilevalue scales, in some embodiments are factored into the size and shapeof the graphical elements being rendered on the screen such that a useror a practitioner is able to visually understand the distinctionsthereof.

In some embodiments, the processed data and/or the generated graphicaluser interface are appended into the electronic health record, such thata practitioner is able to quickly view the results when considering thetype of care and therapy to provide to the individual.

Where individuals may report symptoms of a concussion have resolved,neurological function (brain function) consequences may still bepresent. Existing methods of assessment (neuroimaging, behaviouralassessments) cannot objectively detect these deficits of brain function.Returning to activity/play while functional deficits are still presentcan be dangerous to the individuals and their community's safety (forexample, ability to operate a motor-vehicle safely).

Current approaches of assessment for concussion and acquired braininjury rely heavily on subjective methods of assessment. Reliable,objective data to make an accurate diagnosis and target customizedrehabilitation is required and not yet integrated as standard medicalpractice.

The current standard of assessment for concussion and acquired braininjuries (ABIs) is one or a combination of behavioural orneuropsychological tests, CTs or MRI scans. This gold standard ismissing the most relevant metric for assessing concussion andABIs—objective measurements of brain function. Clinicians today areforced to make decisions on patient treatment based on incomplete andoften irrelevant information for the condition which means patients arenot getting the right treatment fast enough, if at all.

Technical solutions are described herein, in the form of physicalassessment devices, tools, methods, processes, and computer-readablemedia storing machine readable instructions, which when executed by oneor more processors, perform steps of a method. In particular, atechnical approach is utilized by a computer system that is configuredto interoperate with or incorporate brain-sensing devices as well asstimulus presentment devices (e.g., auditory stimulus, visual stimulus,as well as deviant versions thereof).

Accordingly, a system is described that is adapted for performingcognitive health assessments (CHAs) that include innovative approachesof concussion and ABI assessment to provide functional data that is adirect measurement of brain function by using electroencephalography tomeasure EEG data and event related potentials (ERPs).

The approach for performing CHAs compares ERP/EEG data to existingmethods of assessment (behavioural (always), neuro-imaging (whenavailable)) to verify or dispute the finding and how they are relevantto the patient's functional consequences resulting from a concussion oracquired brain injury.

In some embodiments, the tool is a standalone, special purpose machinethat is adapted for use in a clinical setting. The special purposemachine may have specialized software and hardware, such as optimizedintegrated circuits or field programmable gate arrays. For example, thetool may be provided on a medical cart, coupled to a patient (e.g.,following a concussion or a coma inducing incident), and the CHAs aremeasured as stimuli are presented (e.g., sound tones, vibrations, visualstimuli, olfactory stimuli), or between when stimuli are presented.These stimuli are presented even to patients who are otherwiseunresponsive (individuals with locked in syndrome, etc.). Locked insyndrome is a medical condition whereby a patient is aware but may notbe able, or has a limited ability to move or communicate.

Neuroscientific approaches are utilized in establishing the proposedapproaches of various embodiments herein in respect of concussion andacquired brain injuries, and the systems have been developed based onscientifically validated approaches of the Applicants. An objective,sensory-based assessment tool is described herein, automaticallycontrolling stimulus presentation and response tracking to generate acomprehensive patient report on their brain function shown throughperformance data for the purpose of clinical intervention by a clinicalspecialist.

A benefit for patients in a clinical setting is that the objective,sensory-based assessment tool does not suffer from the “noise” caused bya patient's emotions and feelings (e.g., fears of a clinical environmentand worry can, for other tests, lead to a false positive reading, forexample, due to White Coat Syndrome, among others). A benefit for theclinician and/or other stakeholders involved in the patient's case isthe ability to identify patients who are malingering. Other “noise” thatmay be present in subjective tests in relation to jaw clenching, andblinking, may also be accounted for.

In accordance with an aspect, a computing system for cognitive healthassessments is provided, the computing device including at least oneprocessor and computer readable memory.

The computing system includes a sensor apparatus connected to one ormore electrodes coupled to a patient's head, the one or more electrodesrecording brainwave data of the patient, and a stimulus presentationmechanism coupled to one or more sensory output devices (in someembodiments, coupled to the computing system or part of the system), thestimulus presentation mechanism generating a series of programmedstimuli to the patient while the sensor apparatus records the brainwavedata of the patient as the patient receives the series of programmedstimuli.

These electrodes and the stimulus presentation mechanism operate inconcert with one another, based on an automated test protocol.Time-coded data sets are extracted, and brainwave data is correlatedwith event-based timing. The brainwave (i.e., EEG) data may include, forexample, MMN responses, P300 responses, N400 responses, P3a, P3b, N1,among others. Not all embodiments are limited and the above are providedas illustrative examples.

A waveform feature extractor processing engine is provided that isconfigured to process the brainwave data of the patient to extract oneor more waveform features, the one or more waveform features includingfor example, one or more P300 responses and/or one or more N400responses.

A cognitive health assessment controller is configured to record, usingthe sensor apparatus, a first portion of brainwave data of the patientduring a first resting period during which no stimuli are beingpresented to the patient, control the stimulus presentation mechanism topresent a repeated auditory tone (or, in alternate embodiments, visualstimulus or a combination thereof) to the patient; control the stimuluspresentation mechanism to present the repeated auditory tone intermixedwith deviant tones (e.g., two or more different sets of tones), andcontrol the waveform feature extractor to track differences in the oneor more P300 responses recorded in the one or more waveform featuresduring the presentation of the repeated auditory tone to the patient andduring the presentation of the repeated auditory tone intermixed withthe periodic deviant tones.

Deviant tones may include, for example, random words, recognizablesounds, among others. Deviant sounds can include unfamiliar novel sounds(e.g., dog barks, doorbells), non-salient words (e.g., “NSOW”). Visualstimulus may include repeated visual stimuli followed by deviant visualstimuli. A combination thereof may include deviant tones relative to avisual stimulus, or vice versa. Similarly, vibro-tactile stimuli arealso possible, by way of mechanical vibrations (e.g., by way of amechanical instrument coupled to the body of the patient configured suchthat individuals are able to detect or respond to stimuli using a senseof touch. Vibrations may be sensed through resonant materials, etc.).

In another aspect, the cognitive health assessment controller is furtherconfigured to: control the stimuli presentation mechanism to present therepeated auditory tone intermixed with periodic deviant tones, that aredistinct from each other, and track differences in the one or more P300and MMN responses recorded in the one or more waveform features duringthe presentation of the repeated auditory tone presentation intermixedwith the deviant tones and during the presentation of the repeatedauditory tone intermixed with the deviant tones.

In another aspect, the cognitive health assessment controller is furtherconfigured to: control the stimuli presentation mechanism to present oneor more auditory phrases each including one or more nonsensical portionsto the patient; and control the waveform feature extractor to track theone or more N400 responses recorded in the one or more waveform featuresduring or proximate to the presentation of the one or more incongruous,nonsensical or unexpected portions.

In another aspect, the processor is configured to augment the brainwavedata with time-codes contemporaneous or near contemporaneously withpresentation of the series of programmed stimuli.

In another aspect, the series of programmed stimuli further include oneor more outlier tones in addition to the repeated auditory tonepresentation (e.g., different presentation depending on the paradigm ofthe test, for example, for an MMN test for involuntary attention forhealthy human beings, the individual is watching a video and the testexamines the involuntary recognition of the deviant tone while they'rewatching the show, and in another example, if investigating a P300response, the patient is instructed to pay attention to the tones, andpress a button each time the patient hears the deviant tone.

In another aspect, the system includes a video recording device adaptedto obtain video data of the patient as the patient undergoing the test.

In another aspect, the system delivers a sequence of visual stimuli andthe patient is instructed to press a button if a stimulus is repeated totrack one or more N2b responses.

In another aspect, the patient is in an unresponsive state and thesensor apparatus is configured to record the brainwave data of thepatient during one or more additional rest periods where the patient isnot receiving the programmed stimuli.

In another aspect, the brainwave data of the patient during one or moreadditional rest periods, where the patient is not receiving theprogrammed stimuli, is combined with the brainwave data of the patientrecorded proximate in time to the presentation of the programmedstimuli.

In another aspect, the system further includes a display controllercoupled to a display, the display controller configured to render abrain assessment interface including at least a representative mappingof a brain of the patient indicative of one or more functions of thebrain are activated proximate to or responsive to the presentation ofthe series of programmed stimuli. The assessment interface is configuredto provide a decision support interface, capable of aiding practitionersin making informed decisions in respect of a patient's prognosis ortreatment. Specifically rendered displays are generated responsive tosensory readings.

In another aspect, the system further includes a display controllercoupled to a display, the display controller configured to render abrain assessment interface including at least a representative mappingof a brain of the patient indicative of one or more functions of thebrain are activated during one or more rest periods between thepresentation of the series of programmed stimuli.

The brain assessment interface renders diagrams with superimposedwaveforms over one or more reference diagrams, indicative of a severityof injury. Comparison values are provided indicating locations ofwaveform peaks, comparisons against similar demographics or controlgroups, among others. For example, “heat maps” may be generated wheredeviations from a demographic norm are shown, whereby the heat mapsindicate severity and are directed to the brain's ability to functioncertain tasks as opposed to showing physical damage (e.g., not “leftfrontal lobe”) but rather, showing long/short term memory injury mappedto a functional category.

In another aspect, the brain assessment interface renders one or morevisual indicators identifying at least one of a severity of injury, theregion where the functional injury is located within the brain, andwhether the injury is affecting normal brain function. The visualindicators may be rendered over a rendered head or brain or on a scaledgraph.

In another aspect, the system generates one or more statistical analysesin the form of one or more reports.

In another aspect, a method for generating data sets representative ofpotential cognitive activity of a patient is provided, the methodincluding: recording, using a sensor apparatus connected to one or moreelectrodes coupled to the patient's head, the one or more electrodesrecording brainwave (EEG) data of the patient in respect of a brain ofthe patient, a portion of brainwave data of the patient during a firstresting period during which no stimulus is being presented to thepatient; controlling a stimulus presentation mechanism to present arepeated auditory tone or visual image presentation to the patient;tracking, on a processor configured for monitoring data received fromthe sensor apparatus: the one or more N1 and P2 responses to auditorytones or visual images to measure the brain's processing of auditorystimuli or N1 and P2 responses to visual stimuli to measure the brain'sprocessing of the visual stimuli; controlling the stimulus presentationmechanism to present one or more auditory or visual phrases eachincluding one or more nonsensical, or otherwise inaccurate or unexpectedportions of a sentence to the patient; tracking, by the processor: theone or more N400 responses recorded in one or more waveform featuresduring or proximate to the presentation of the one or more nonsensicalportions of the sentence to measure the brain's ability to process wordand phrase meanings, sentence grammar and discourse; controlling thestimulus presentation mechanism to present one or more incongruous,unexpected or otherwise surprising words or sentences within a languagecontext; tracking, by the processor: the one or more N400 responsesrecorded in one or more waveform features during or proximate to thepresentation of the one or more incongruous, unexpected or otherwisesurprising words or sentence pairings to track the brain's ability toprocess word and phrase meanings, and vocabulary recognition;controlling the stimulus presentation mechanism to present repeatedtones or visuals intermixed with deviant tones or visuals whilepresented in concert with a constant tone or visual; and tracking, bythe processor: the one or more MMN responses recorded in one or morewaveform features during or proximate to the presentation of the one ormore deviant tones or visuals to track the brain's ability to respond toenvironmental changes that are not actively attended; controlling thestimulus presentation mechanism to present repeated tones or visualsintermixed with deviant tones or visuals; tracking, by the processor:the one or more P3a responses recorded in one or more waveform featuresduring or proximate to the presentation of the one or more deviant tonesor visuals to track the brain's ability to respond to stimulus deviance;controlling the stimulus presentation mechanism to present repeatedtones or visuals intermixed with deviant tones or visuals while thepatient has been instructed to actively recognize or respond to thedeviant tones or visuals; tracking, by the processor: the one or moreP3b responses recorded in the one or more waveform features during orproximate to the presentation of the one or more deviant tones orvisuals to track the brain's ability to focus one's attention on a task;controlling the stimulus presentation mechanism to present complexvisual or auditory pattern stimuli, at least one of which are repeatedthroughout the sequence while the patient has been instructed toactively recognize or respond to the repeated visuals or auditory tonesor patterns; tracking, by the processor: the one or more P3b responsesrecorded in one or more waveform features during or proximate to thepresentation of the one or more repeated tones or visuals or patterns totrack the brain's ability to temporarily hold information available forprocessing; controlling the stimulus presentation mechanism to presentcomplex visual or auditory pattern stimuli, some of which are repeatedthroughout the sequence while the patient has been instructed toactively ignore the specific visuals or auditory tones or patterns, andrecognize or respond to the alternate repeated visuals or auditory tonesor patterns; tracking, by the processor: the one or more N2b responsesrecorded in one or more waveform features during or proximate to thepresentation of one or more repeated tones or visuals and reaction toone or more repeated tones or visuals to track the brain's ability towork through complex processes to enable complex behaviour; differencesin the N1, P2, N400, MMN, P300 (P3a/P3b), N2b responses recorded in theone or more waveform features during the presentation of the repeatedauditory tone or visual image presentation to the patient and during thepresentation of the repeated auditory tone or visual image intermixedwith the deviants; the one or more P3b responses recorded in one or morewaveform features in response to complex visual pattern stimuli, some ofwhich are repeated throughout the sequence; and generating a data setbased on the extracted waveform features, the data set including datafields corresponding to at least one of an automatic attention metricbased at least on the differences in the one or more MMN responses, areactive attention metric based at least on the differences in the oneor more P3a responses, a concentration metric the differences in the oneor more P3b responses, a working memory metric based at least thedifferences in the one or more P3a responses, or an executive functionmetric based at least on the differences in the one or more N2bresponses.

In another aspect, the generated data set is appended to an electronichealth record data structure associated with the patient.

In another aspect, a normalized and transformed data set is generatedand appended to an electronic health record data structure associatedwith the patient.

In another aspect, the generated data set is transmitted to a medicalmonitoring apparatus coupled to the patient, and wherein the medicalmonitoring apparatus modifies one or more or operating parametersresponsive to the data fields, the one or more operating parametersincluding at least a polling frequency of the medical monitoringapparatus.

In another aspect, the generated data set is transmitted to a medicalmonitoring apparatus coupled to the patient; and the medical monitoringapparatus, responsive to a determination that the data fields indicatingthat one or more of the automatic attention metric, the reactiveattention metric, the concentration metric, the working memory metric,or the executive function metric are greater than a pre-definedthreshold, is configured to generate an alert or notification.

In another aspect, the processor is a plurality of distributed computingresources that operate in concert to process the brainwave data of thepatient.

In another aspect, the patient is elderly, disabled, diagnosed with apotential concussion or acquired brain injuries, is in an unresponsivestate or is comatose.

In another aspect, the patient is an individual diagnosed with apotential concussion or acquired brain injuries, wherein the differencesrelating to one or more MMN responses, the one or more N2b responses orthe one or more P3b responses are used to indicate a level of severityof the potential concussion or acquired brain injuries.

In another aspect, the patient is in an unresponsive state and thesensor apparatus is configured to record the EEG data during one or moreresting periods where the patient is not receiving the series of theprogrammed stimuli.

In another aspect, the patient is in comatose and the sensor apparatusis configured to record the EEG data during one or more resting periodswhere the patient is not receiving the series of the programmed stimuli.

In another aspect, system is used in relation to general brain healthtracking.

In this respect, before explaining at least one embodiment in detail, itis to be understood that claimed embodiments is not limited in itsapplication to the details of construction and to the arrangements ofthe components set forth in the following description or illustrated inthe drawings. Other embodiments are possible and carried out in variousways. Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments will now be described, by way of example only, withreference to the attached figures, wherein:

FIG. 1A is a schematic diagram depicting a physical apparatus forimplementing a cognitive health assessment, according to someembodiments.

FIG. 1B is a schematic diagram depicting EEG equipment utilized in thesystem, according to some embodiments.

FIG. 2 is a block schematic diagram depicting example components of asystem configured for cognitive health assessment, according to someembodiments.

FIG. 3A is a flow diagram depicting an example method for preparing fora cognitive health assessment, according to some embodiments.

FIG. 3B is a flow diagram depicting an example method for preparing fora cognitive health assessment, according to some embodiments.

FIG. 4 is a flow diagram depicting an example method for a cognitivehealth assessment tailored for patients in comas, according to someembodiments.

FIG. 5A is a flow diagram depicting an example method for a cognitivehealth assessment tailored for unresponsive wakefulness syndrome (UWS)(vegetative state) patients (new patients), according to someembodiments.

FIG. 5B is a flow diagram depicting an example method for a cognitivehealth assessment tailored for unresponsive wakefulness syndrome(vegetative state) patients (follow up assessment for patients who haveemerged from coma), according to some embodiments.

FIG. 5C is a flow diagram depicting an example method for a cognitivehealth assessment tailored for unresponsive wakefulness syndrome(vegetative state) patients (existing patient follow-ups), according tosome embodiments.

FIG. 6A is a flow diagram depicting an example method for a cognitivehealth assessment tailored for patients with concussions, according tosome embodiments.

FIG. 6B is a flow diagram depicting an example method for a cognitivehealth assessment tailored for clients seeking a general cognitivehealth assessment, according to some embodiments.

FIG. 7A, FIG. 7B, FIG. 7C, and FIG. 7D show example brain sensorwaveform diagrams, according to some embodiments. FIG. 7A shows auditoryN1/P2, MMN waveforms, FIG. 7B shows visual N1/P2, P3b waveforms, FIG. 7Cshows N2b, P3a, P3b waveforms, FIG. 7D shows a N4 waveform.

FIG. 8A, FIG. 8B, and FIG. 8C are example interface screens comparingbrainwave patterns of a patient compared to a concussed control group,according to some embodiments. FIGS. 8A-8C illustrates topographicalmapping generated throughout waveform data collection. Areas of thebrain ‘light up’ at the time of an ERP response. Although one may seeirregular responses in waveforms, one may not see irregularities on thetopographical maps or vice versa. Therefore, topographical maps confirmthe elicitation of an ERP. FIGS. 8A-C shows grand-averaged P300 protocolwaveforms and their respective scalp distributions recorded at Cz,evoked by target stimuli, for each group (Controls Left, Concussedperson, Right). FIG. 8A: N1, N2b, P3a, and P3b components evoked in theFrequency condition. (B) FIG. 8B: N1, N2b, P3a, and P3b componentsevoked in the Duration condition. (C) FIG. 8C: N1, N2b, P3a, and P3bcomponents evoked in the Intensity condition.

FIG. 9 shows example graphical results of a patient's brain functiondata from a cognitive health assessment report

FIG. 10 shows example recovery progress tracker from a cognitive healthassessment report through repeat testing.

FIG. 11 is a block schematic of an example computing device, accordingto some embodiments.

FIG. 12 is a method diagram of an example process, according to someembodiments.

In the drawings, embodiments are illustrated by way of example. It is tobe expressly understood that the description and drawings are only forthe purpose of illustration and as an aid to understanding, and are notintended as a definition of the limits of the claimed embodiments.

DETAILED DESCRIPTION

The current standard of assessment for concussion and acquired braininjuries (ABIs) is one or a combination of behavioural orneuropsychological tests, CTs or MRI scans. This gold standard ismissing the most relevant metric for assessing concussions andABIs—objective measurements of brain function. Clinicians are forced tomake decisions on patient treatment based on incomplete and oftenirrelevant information for the condition which means patients are notgetting the right treatment fast enough, if at all.

An industry-first objective concussion and acquired brain injuryassessment system and corresponding methods and computer-readable mediaare described herein. The approach delivers quantitative insights onpatients' brain function that inform, accelerate, and track recoverywith unprecedented precision. Applicants note that there is no otherconcussion and acquired brain injury assessment clinically availablethat provides legitimate measurement of brain function to objectivelyand specifically inform cognitive rehabilitation in all core functions:auditory processing, visual processing, information processing,automatic attention, reactive attention, concentration, memory, languagecomprehension and executive function.

An improved system is provided that includes, in some embodiments, brainsensing devices (e.g., electrodes), stimuli presentment mechanisms(e.g., display screens, vibration motors, speakers, olfactory releasedevices), and computer instruction sets for controlling measurement andstimuli presentment is described. The stimuli presentment is controlledto include various repeated, transformed and/or deviant versions ofstimuli, and automatic early attentional brain mechanisms and/orconsciously controlled attention mechanisms are triggered by suchrepeated, transformed and/or deviant versions of stimuli. The trackeddata, in some embodiments, is processed to extract one or more datasets, which can be used for modifying device operational parameters,updating electronic health records, or as inputs for controllingrendering of a graphical user interface, according to variousembodiments.

Traditional EEG is used in clinical practice, and quantitative EEG(qEEG) is becoming more common, to assess seizures and sleep disorders.In some cases, they may also be used to help assess severe braininjuries.

EEG testing available in most clinics and hospitals is capable ofcapturing ongoing brain-signals and oscillations passively (e.g., alphaand beta brain waves), and is commonly known as “resting state”.Clinicians typically ‘read’ traditional EEG scans free-form. qEEG is animprovement on traditional EEG in the way it analyses a patient's data,statistically comparing their results to healthy controls.

The detection of abnormal brain function as a result of a concussion oracquired brain injury using resting state testing methods withtraditional EEG or qEEG is only possible within the first 72 hourspost-injury. Otherwise, these methods cannot find useful informationregarding a concussion or acquired brain injury because the perturbationof resting brain waves return to normal after 72 hours.

Cognitive health assessments as described herein use EEG equipment, andthe use of these systems expand the range of measurable brain activityby enabling direct measurement of the brain's responses to stimulationand cognitive tasks. The approach described includes specificcomputer-controlled sequences and controlled measurements thereof. Insome embodiments described herein, enhanced EEG equipment is utilizedthat is research grade, and capable of expanding the range of measurablebrain activity.

The embodiments described herein tracks a patient's active responses toa range of tasks and stimuli instead of only at a resting state. Thesystem can include stimuli generating/presenting mechanisms which areselectively actuated in accordance with a specific process or sequenceof cognitive function measurements through brain electrical impulsesensing apparatuses. Event related potentials (ERP) measures are trackedto assess different levels of conscious processing and presence of signsof a conscious state predictive of subsequent emergence or potentialundiagnosed injury.

Stimuli can include regular stimuli, and generated stimuli that isspecifically adapted to be dissonant, incongruous, unexpected,surprising, or deviant (e.g., 2400 tones can be selected, for example,with 82% standard tones (at around 50 ms, 1000 Hz, 80 dB), and threetypes of deviant tones at 6% of the population each). Deviants, forexample, can include duration deviants (e.g., 125 ms), frequencydeviants (e.g., 1200 Hz), and intensity deviants (e.g., 90 dB SPL).

This active engagement with stimuli throughout testing, which can bethought of as a stress-test or performance-test for the brain, is howthe system is able to provide objective data on specific brain functionsthat require rehabilitation, instead of generalizing the higher-levelbrain function of a patient at resting state—to see if their brainfunction looks normal or injured.

Information on whether a brain is functioning normally or not isvaluable in making an initial diagnosis within the first 72 hourspost-injury, but does not allow clinicians to objectively informtargeted cognitive rehabilitation plans. Cognitive health assessmentscan be run at various times, whether an injury happened 2 days ago or 20years ago, to quantify the functional issues and objectively inform atreatment path.

Further explaining the novel-ness of the approach disclosed as comparedto traditional EEG, qEEG and other newer EEG based cognitive assessmenttools, is in the combination of ERPs recorded, to be able to provide afulsome report on each core area of cognitive function required toinform a complete and customized cognitive treatment plan, unique toeach patient. Many newer EEG technologies rely on a single ERP (e.g.,P300), or a combination of up to three (e.g., N1, P300, N400) tounderstand if a patient's brain function is abnormal as compared tohealthy controls, indicating a concussion or brain injury is present.

However, these measurements alone cannot provide the level of insightrequired to create an objective, unique and customized treatment planfor patients. An improved system for assessment of brain injuries,actual, or potential, is disclosed in some embodiments.

The system is a computer implemented system that provides physicalassessment devices, tools, methods, processes, and computer-readablemedia storing machine readable instructions, which when executed by oneor more processors, perform steps of a method. The system is adapted forperforming cognitive health assessments (CHAs) that include innovativeapproaches of concussion and ABI assessment to provide functional datathat is a direct measurement of brain function activity by using EEG andmeasuring activity including: event related potentials (ERPs), powerspectrum, connectivity, coherence, frequency following responses andrelated stimulus contexts.

The system, in alternate embodiments, is a software-as-a-serviceplatform (SaaS) hosted on physical distributed resources that areaccessible through communications networks. In a SaaS implementation,analyses may be conducted despite local unavailability of extensivecomputing resources. For example, a mobile hospital or a remote hospitalor clinic would be able to conduct the tests on physical devices (e.g.,a processor, stimulus effectors [e.g., speakers], data recorders,sensors) that are locally present. The information may then betransmitted to a distributed resource or implementation (e.g., a cloudof computing resources) for processing. Outputs from the systemindicative of an analysis, or assessment are generated, and potentiallyprovided back to the mobile/remote hospital or clinic. The SaaS platformmay be configured to capture the raw bio-signal data and present adecision support interface that a practitioner can readily view andinteract with in preparing a diagnosis.

For more severe ABIs, and even sometimes for a suspected concussion ormild traumatic brain injury, CT or MRI scans can be one of the firstmethods of assessment. These imaging scans are relevant for showingstructural damage—fractures to the skull, lesions in brain tissue andbrain bleeds. If the scans show structural damage, it can be indicativethat functional damage also exists, but further examination is requiredto confirm this. It is possible for a patient to have structural damagebut still be ‘normal’ in terms of function, and it is also possible fora patient to not show any signs of structural damage but have severefunctional damage that would not be uncovered by theseassessments—meaning that traditional neuroimaging risks missing theconcussion or mild traumatic brain injury, or improperly assessing amore traumatic injury—potentially leading to a misdiagnosis for thepatient.

In other approaches, those further assessments would beneuropsychological testing or behavioural testing as an attempt tomeasure functional damage, but their results remain subjective andrequire expert best judgement to diagnose. The tests described herein,in some embodiments, expand on industry standard neuropsychologicalassessments and are adapted to provide objective analyses of brainfunction.

The responses to neuropsychological and behavioural testing are moreaccurate through cognitive health assessments according to variousembodiments because they turn subjective measurements(neuropsychological or behavioural test results on their own) intoobjective measurements (neuropsychological testing while measuring ERPswith EEG and related measures, and compared to behavioural tests, andneuroimaging results when available).

When a neuropsychologist or clinician using behavioural tests assess apatient, the reliance on behaviour alone leaves a margin for error basedon the patient's engagement with the testing and theneuropsychologists/clinicians interpretation of the patient's answers.

With EEG being used to complete these measurements for actualelectrophysiological brain responses, it is possible to obtain resultsthat are not dependent on interpretation of information that is itselfsubjective and demonstrably, situationally, insensitive rather thanobjective. The results provide clinicians with objective data on brainfunction. This makes cognitive health assessments results more usefulthan classic neuropsychological or behavioural tests because it hasremoved the potential for human error. Brain responses cannot bemanipulated in EEG testing, whereas with neuropsychological, or anybehavioural tests, the patient can lessen or increase the measurement oftheir severity of injury based on how they respond to testing—be thatmanipulation intentional or not.

As noted herein, the brain responses measured can include obligatorysensory responses that can be used to assess, through the selectivepresentment of stimuli, objective evidence of brain function despite apatient not showing signs otherwise. Obligatory responses can be evokedby the selective presentment of stimuli, such as deviants, and dissonantsounds/images, and nonsensical lexical pairings, and the descriptionsherein relate to mechanisms for tracking electrical brain impulses inrespect of “pre-attentive cognitive processes”. Accordingly, the signalstracked herein are obtained without requiring a subject's activeinvolvement. In particular, the P300 and the MMN have a good correlationwith coma awakening.

Seven examples of how the cognitive health assessments positivelyintervened/provided added value with a patient's concussion or acquiredbrain injury journey are as follows:

CONCUSSION EXAMPLE 1: Patient “A” scored highest on the behaviouralassessment portion of the applicant's study comparing the largestdataset of living ex-pro football players to healthy controls to datethat leveraged EEG to measure brain function. The study comparedbehavioural and neuropsychological assessment, to functional MRI and theEEG protocol of the cognitive health assessment. When Patient “A” scoredthe highest on the behavioural assessment, traditionally this wouldindicate he ranked as the “most damaged/injured” as a result of physicalimpact he incurred throughout his professional football career,indicating long-term cognitive damage.

However, the cognitive health assessment (which recorded his brainfunction as it pertained to: Auditory Processing (N1/P2), VisualProcessing (N1/P2), Information Processing (all ERPs), AutomaticAttention (MMN), Reactive Attention (P3a), Concentration (P3b), andWorking Memory (P3b) in this case), provided objective data that provedPatient “A” was in fact experiencing no functional consequences from histime as a professional football player, and that his high score on thebehavioural assessment portion of the study was due to hisanxiety/paranoia about his condition following the growing prevalence ofconcussion issues surfacing in mainstream media. In other words, when heforgot why he walked into the kitchen at home it was simplyforgetfulness, and not due to functional damage incurred during hisprofessional football career.

Had Patient “A” not received the cognitive health assessment, he wouldnot have received verification if his suspicions/high-scoringbehavioural tests were accurate and if rehabilitation was required.

The cognitive health assessment provided objective evidence thatdemonstrated he was, in fact, cognitively healthy when compared tomatched controls—a finding that was in contradiction to his subjectiveimpressions. Patient “A” thus received confirmation that hisself-reported symptoms were linked to psychological conditions due tofear of the unknown of how is career may have impacted his brain health.The objective confirmation of the health of his brain function providedmental relief and guided that if ongoing symptoms persisted his nextsteps in treatment could be appropriately tailored to his needsregarding his anxiety.

The MRI portion of the study supported the EEG data that this patientalso had no structural damage, for example measured by diffusion tensorimaging measures. The patient was in fact the healthiest participant onboth the EEG cognitive health assessment and MRI portions of the study.

CONCUSSION EXAMPLE 2: Patient “B” was struck by a car as a pedestrian.They lost consciousness at the scene and were diagnosed with a“moderate-to-severe” concussion at the hospital the next day as a resultof their physical and behavioral examination. They went on to have twoCT scans, an optometrist exam and a psychological assessment.

The CT scans both turned up negative and the optometrist exam was clear(indicating no concussion by the hospital's standard protocol, meaningthe initial diagnosis was unclear.) The psychological assessmentuncovered a history of mood disorder. Due to Patient “B”'s history ofmood disorders, many of their symptoms the patient associated with theconcussion incident were attributed to the pre-existing mood conditions.

This led to Patient “B” being treated with pharmaceutical interventionand counselling being prescribed as their treatment strategy. Patient“B”'s symptoms worsened, compounding pre-existing anxiety anddepression. Patient “B” could not return to work and withdrew fromsocializing due to issues with concentrating, performing day-to-daytasks and following conversation.

Two years post-injury, Patient “B” was still suffering from symptoms notpresent prior to the injury and others compounded since the injury.Patient “B” sought out alternate treatment and as a result were enrolledin a pilot program performed by the applicants in partnership with arehabilitation group. Patient “B”'s initial cognitive health assessmentconfirmed the patient suffered from cognitive dysfunction in AutomaticAttention (MMN), Reactive Attention (P3a), Concentration and WorkingMemory (P3b)—all results showed “moderate deficiency” or “severedeficiency” as compared to healthy controls.

The rehabilitation group prepared a cognitive therapy plan based on thedeficits indicated, and Patient “B” embarked on a 10 week rehabilitationplan with their occupational therapist. At the end of the treatmentplan, Patient “B” was reassessed using the cognitive health assessment.

The follow-up report showed improvements in cognitive functioning in allof the areas previously identified as below a healthy norm. AutomaticAttention, Reactive Attention and Memory results all fell within thehealthy range and were recorded as “normal” compared to healthycontrols, and Concentration performance was only slightly below, whilestill falling into the “moderate deficiency” category as compared tohealthy controls.

Throughout Patient “B”'s experience with the cognitive healthassessments and subsequent cognitive rehabilitation (which they wouldnot have received without the cognitive health assessment), they alsosaw gradual improvements in their mood conditions due to theconfirmation that cognitive deficits as a result of their injury were infact present.

This confirmation allowed the patient to better understand theirsymptoms and provided them motivation with tangible goals and actions toact upon to better their mental and cognitive health. Patient “B” iscontinuing to work on strategies for Concentration, and has begun tore-engage socially, and returning to work on a part time basis.

The objective data provided by the cognitive health assessment allowedPatient “B”'s care team to take a multidisciplinary approach which leadto Patient “B” being more motivated and inspired to work on theirrecovery, knowing they would be able to quantify their improvements withthe cognitive health assessment's objective results.

CONCUSSION EXAMPLE 3: Patient “C” is a unique case who was a part of theapplicants' study on ex-pro football players as a healthy control.Patient “C” later was a party to a motor vehicle accident that lead to adiagnosis of a mild concussion. The diagnosis was given following aphysical and behavioural examination at hospital.

A CT scan was run, and the results were negative. Patient “C” wasmonitored throughout the first four weeks of their concussion, butsymptoms (including headache, dizziness, cognitive and physical fatigue,and tinnitus) persisted. After months of symptoms not resolving, Patient“C” sought out a cognitive health assessment. Patient “C” was tested forAuditory Processing (N1/P2), Visual Processing (N1/P2), InformationProcessing (all ERPs), Language Comprehension (N400), AutomaticAttention (MMN), Reactive Attention (P3a), Concentration (P3b), WorkingMemory (P3b) and Executive Function (N2b/P3b). “Moderate deficiency” wasidentified for Automatic Attention, Reactive Attention, Working Memoryand Executive Function. “Severe deficiency” was indicated forConcentration.

All of Patient “C”'s core cognitive functions were confirmed to havebeen affected by the motor vehicle accident. Patient “C” has enrolled ina 10 week rehabilitation plan to work on the deficits identified andwill be reassessed mid-rehabilitation and post-rehabilitation to supporttheir clinician's ability to medically clear the patient to return toregular activity - for them, meaning full-time work and recreationalsports.

Both Patient “B” & “C” had struggled to manage their symptoms and lackof objective evidence when working with their employers on dealing withtheir injuries. The cognitive health assessments provided both objectiveevidence to the employers of the injury and informed them of which areasthe patients were suffering in, allowing for accommodations to bestrategically planned for as they worked through their recovery.

Both patient “B” and “C” qualified for motor-vehicle insurance coveragefor their cognitive health assessments and subsequent treatment based onthe objective information the cognitive health assessments provided,which they previously had not been deemed eligible for due to lack ofevidence of the injury and its impact on the patients' lives.

The objective data provided in cognitive health assessment reports allowclinicians to pinpoint and address the root of the cognitive impairmentsa patient is dealing with, in addition to treating observable/reportedsymptoms and functional impairments. Knowing areas of cognitive deficitis essential for rehabilitation professionals to facilitate thepatient's return to pre-accident functioning and improve their qualityof life, as proven to have been the case in all of the concussionexamples provided herein.

CONCUSSION EXAMPLE: Patients who show no physical damage according toMRI scans, score “healthy” on behavioural and neuroimaging assessments,but know there is “something wrong” i.e. they are concussed but have noother methods of assessment to consider to objectively confirm theinjury.

Cognitive health assessments can detect functional consequences ofconcussion even when patients show no signs of concussion from today'sgold standard of assessment. Function is the most relevant metric andnot currently leveraged in an objective method in today's gold standardof concussion assessment.

ABI EXAMPLE 1: Patient “D” was struck by a car while on a bike andthrown 30 meters. They were deemed catastrophically injured with novitals on the scene. After being resuscitated by paramedics and operatedon in hospital for 12 hours to stabilize their condition, Patient “D”was confirmed to have sustained severe brain and abdominal injuries anddiagnosed as being in a vegetative state—now referred to as unresponsivewakefulness syndrome (UWS) (vegetative state).

After slipping in and out of a coma for 10 days, Patient “D” wastransferred to a step-down clinic where they regained consciousness andwere tested by the principal neurologist at the facility. Theneurologist was unsure of the original diagnosis of vegetative state dueto how the patient was responding to their testing.

The scores they most commonly relied on, the Coma Recovery Scale (CRS-R)and Glasgow Coma Scale (GCS) scores classified the patient asvegetative, but the neurologist was not convinced. The neurologistreferred Patient “D” to the applicants to perform a cognitive healthassessment to determine if brain function could be detected. The MMN(Automatic Attention), P300s (Reactive Attention, Concentration, WorkingMemory), and N400 (Language Comprehension) tests as described under theUWS protocol in FIG. 5A were run. The results confirmed not only wasbrain activity present, but responses were fairly strong, indicatingthat Patient “D” had the potential to respond to rehabilitation effortsby their care team.

The results of the cognitive health assessment that objectivelysupported the neurologist's belief that the patient was not in fact in avegetative state, granted the patient the opportunity to be transferredto the acquired brain injury unit for rehabilitation. Without theobjective results of the cognitive health assessment, the patient wouldnot have been awarded the opportunity for treatment due to an absence ofobjective data to indicate they had the potential to be rehabilitated.

Post-injury, Patient “D” lives at home with their family, has regainedhealthy cognitive activity, has limited ability to communicate throughspeech but can communicate effectively with care providers and family,and exercises seven days a week to continue to build physical strength.

ABI EXAMPLE 2: Patient “E” suffered from a gunshot wound to the head.Following extensive surgery to stabilize their condition, the patientwas deemed vegetative and placed in palliative care. The family ofPatient “E” believed the patient was inaccurately diagnosed, and wasactually in a “locked-in” state—a condition in which a patient is awarebut cannot move or communicate verbally due to complete paralysis ofnearly all voluntary muscles in the body except for vertical eyemovements and blinking.

After 2 years of little progress, the family was struggling with thedecision to keep Patient “E” on life support, which lead to the family'srequest for a cognitive health assessment to provide them with objectivedata to assist their decision. The MMN (Automatic Attention), P300s(Reactive Attention, Concentration, Working Memory), and N400 (LanguageComprehension) tests as described under the UWS protocol in FIG. 5A wererun. The results demonstrated that despite significant brain injury,

Patient “D” retained some cognitive functioning in Automatic Attention,Reactive Attention and Memory. Language Comprehension was not foundpresent which was likely due to their lack of response in VisualProcessing, due to the nature of how the test was administered withvisual stimuli. Given the identified complications with sight, follow-upto determine Language Comprehension through auditory stimuli wasrecommended.

As a result of Patient “E”'s cognitive health assessment, their careteam and family began engaging with Patient “E” in ways they knew wouldbe more effective given their abilities, e.g., communicating verballyinstead of relying on recognition through sight. Therapeuticintervention is being pursued based on the confirmation of consciousawareness in Patient “E”.

ABI EXAMPLE: Unresponsive wakefulness syndrome (UWS, previously referredto as vegetative state) and coma patients were traditionally impossibleto assess to determine their outcome potential due to their inability toengage with a healthcare professional's assessment.

Historically, rehabilitation facilities for this patient population haverelied heavily on patients' progress through trial and errorrehabilitation methods. With the cognitive health assessment reports,the rehabilitation providers are able to assess a patient's level ofconsciousness to predict their outcome and identify their “potentialoutcome potential”, thus assisting families of patients who need to makea decision on proceeding with treatment, and provide direction for thosewho show potential based on their results.

For unresponsive wakefulness syndrome (UWS) (vegetative state) or comapatients specifically, the cognitive health assessment reports help thepatients' families understand, with the objective data, what levels ofconsciousness the patient has, facilitating a confident decision intheir next steps to pursue treatment, or take the patient off of lifesupport. When cognition is identified meaning the patient has rehabpotential, the reliable, quantitative data has helped facilitate thepatients' families securing funding for their treatment.

A patient in an unresponsive wakefulness syndrome (UWS) (vegetativestate) or within a coma may have some conscious awareness but be unableto respond due to sensory or perceptual impairments, aphasia, motorimpairments, subclinical seizure activity, pain, fluctuating arousal,fatigue, and a range of other problems. With conventional assessmenttools such a patient would receive an inaccurate diagnosis of UWS (VS).This scenario is far from uncommon.

Without the cognitive health assessment reports, the healthcareproviders and families need to make decisions for treatment based off ofthe behavioural assessments mentioned above—resulting in misdiagnosisrates for UWS (VS) that are consistently estimated at about 40% (Andrewset al., 1996; Childs et al., 1993; Schnakers et al., 2009a,b).

In one assessment, cognitive health assessment reports generated by theanalyses produced by the systems and procedures confirm if a concussionor ABI is present, the severity of the injury, specific domains offunctional (e.g. neurocognitive) deficit incurred by the injury, whichaid in determining areas of focus for rehabilitation.

This allows certainty in results for clinicians to provide targeted andtimely methods for rehabilitation. By reducing the need for multipletests to confirm or disprove a concussion or ABI, patients can get thehelp they need when they need it—as early into their recovery path aspossible. The certainty additionally allows clinicians and patients toavoid trial and error in rehabilitation methods, instead focussingsolely on the areas of cognitive rehabilitation the patient needs fortheir unique injury by following the functions identified as below thehealthy norm in their cognitive health assessment report.

Applicants have developed the technology which originated from adaptingthese methods to assist the analysis of both coma and unresponsivewakefulness syndrome (vegetative state) patients. The system's abilityto measure brain function with non-participatory requirements on thepatient's behalf, has supported to confirm if brain function waspresent, which facilitated the patient getting into rehabilitation plansthat have improved their conditions. Without this ability to identifybrain function in patients who are unable to outwardly communicate, aclinic would not have been able to identify if these patients werecapable of rehabilitation and eligible for it.

For coma patients specifically, cognitive health assessment according tosome embodiments described herein have also been leveraged to confirmwhen a coma patient does in fact not have any measurable brain function,providing certainty to both the patient's clinicians and family thatthey are able to let go of their loved one without fear of missing achance for rehabilitation to improve their condition.

Aside from coma, unresponsive wakefulness syndrome (vegetative state)and concussion, the technology has also been used to assess disorders ofconsciousness (DOC) and pediatric communication impairments (PCI).

FIG. 1A is an example system 100A for conducting cognitive healthassessment that compares ERP/EEG data to methods of assessment(behavioural (always), neuro-imaging (when available)) to verify ordispute the finding and how they are relevant to the patient'sfunctional consequences as a result of a concussion or acquired braininjury.

In FIG. 1A, a Stimulation Computer is shown as an example interfacedevice, whereby the Stimulation Computer may be configured to presentstimuli to the patient by way of headphones, display controllers, etc.The Stimulation Computer may include input receiving devices, such asmicrophones and a video capturing device, such as a webcam. These inputreceiving devices may be configured to capture responses, voluntary orinvoluntary, of the patient during the course of the testing. In someembodiments, involuntarily responses may be captured during the courseof rest periods between stimuli, among others.

The patient is coupled with one or more electrodes (e.g., coupled to thepatient's head) to capture brain function data, including event relatedpotentials (ERPs). The electrodes may be connected to the patient in theform of a cap or other headgear, or individually.

During a test session, the computing system facilitates a series of theadapted neuropsychological tests for computer presentation, whereby astimulus presentation program is used to take the patient through aseries of steps.

The Stimulation Computer runs a presentation process to generateprogrammed stimuli (auditory or visual), the patient hears or seesstimulus as their EEG is recorded by the electrodes (e.g., in a cap),the electrode data are amplified using the EEG equipment and saved bythe Data Visualization Computer, overlaid with stimulus markers(transferred to the Data Visualization Computer directly by theStimulation Computer).

These stimulus tests are programmable by the user or an administrator,and the ERP tests/paradigms are programmed to record the ERP responsesand correlating behavioural responses (where the patient may be requiredto click or press a button) at the same time to avoid needing totime-match the two after the fact.

FIG. 1B illustrates example hardware 100B that can be utilized tocapture the brain excitation data, including different types ofelectrodes, etc. The hardware may need to be adapted based on the sizeand profile of the individual's head. Example EEG hardware may includeBiosemi™ system, a BrainProducts™ system or Compumedics Neuroscan™system, among others.

Paradigm stimulation are delivered through the Stimulation Computer andheadphones to the patient to stimulate electrical brain activityresponsive to the paradigm stimulus. The electrodes capture data values,which are amplified, and provided to an acquisition mechanism andtransferred to a Data Visualization Computer.

A Stimulation Computer is configured to provide stimulus markers, and insome embodiments, receive inputs through a mouse or control pad. Thestored data is processed to correlate the data collected by the DataVisualization Computer in concert with the data collected with theStimulation Computer such that brain potentials (responses) may betracked and processed in time-coordination with the presentation of thestimuli.

The tests/paradigms have been designed with the abilities of the patientin mind (embodiments can include: auditory and vibro-tactile for coma;auditory, vibro-tactile and visual for unresponsive wakefulness syndrome(vegetative state); auditory, visual and vibro-tactile for concussion).Once set up and turned on, the tests are automated. The settings andscript have been developed by the Applicants for the ease of analysis bystatistical analysts, and the output from a statistical analysissoftware (e.g., R Studio) is designed for ease of reading by a reportgenerator software.

FIG. 2 illustrates a separated Stimulation Computer and DataVisualization Computer mechanism 200, according to some embodiments.Note, however, that the Stimulation Computer and the Data VisualizationComputer may be the same computer or different computers, in alternateembodiments.

In some embodiments, the Data Visualization Computer and the StimulationComputer, can be transported such that the system can offer remotetesting (on location), requiring a quiet room for patient, and space fora tester to monitor. If only 1 room is available, a patient can, forexample, face a wall to minimize distractions and tester sits behind amonitor.

FIG. 3A is an example process diagram illustrating an initializationmethod 300A, according to some embodiments. A tester opens acquisitionsoftware on the Data Visualization Computer in a control room. Thesettings within the acquisition software for test measurements are setto be able to ‘zoom in’ on data produced by the testing paradigms set upin the presentation software (tests the patient is run through).

FIG. 3B is an example process 300B to start a presentation on theStimulation Computer, and to ensure the data is being recorded andviewable on the Data Visualization Computer.

In FIG. 4 , FIG. 5A, FIG. 5B, FIG. 5C, FIG. 6A, and FIG. 6B, examplemethods 400, 500A, 500B, 500C, and 600 are illustrated of improvedcognitive health tests associated with comatose patients, unresponsivewakefulness syndrome (vegetative state) patients, concussed patients,and for general health testing.

These tests illustrate an improved, objective approach to providingassessment offerings. In additional embodiments, the tests may also beutilized for conducting mental competency assessments, elderlycompetency assessments, neurodevelopmental disorder competencyassessments, measuring drug effects on brain function, and general brainhealth tracking.

The cognitive health assessment is adapted to test for:

-   -   the severity of the concussion or ABI;    -   specific domains of brain function (neurocognitive) deficit        incurred by a concussion or ABI;    -   potential areas of focus for rehabilitation; and    -   tracking rehabilitation impact on brain function recovery.

In some embodiments, the statistical analysis, data collection andreport generation steps in the assessment process are automated toreduce the turnaround time from testing to clinicians being able toreview and diagnose the concussion or ABI and provide next steps forrehabilitation. Computer implemented embodiments are described hereinthat include a stimulus machine controlled to operate in concert with abrain function scanning machine to conduct a series of tests. The testsare designed to target different brain (cognitive) functions includinglanguage processing and comprehension abilities, high-level attentionand vigilance skills, memory integrity, language comprehension,executive function, auditory and visual function, and functions of thesomatosensory system (conscious perception of touch, pressure, movement,position, vibration, pain that development from contact (broadlydefined) with receptors in the skin, muscles, tendons, joints, etc.).

An improved approach is described in various embodiments, whereinphysical assessment devices, tools, methods, processes, andcomputer-readable media storing machine readable instructions, whichwhen executed by one or more processors, perform steps of a methodperforming cognitive health assessment that include innovativeapproaches of concussion and ABI assessment to provide functional datathat is a direct measurement of brain activity by using EEG andmeasuring event related potentials (ERPs).

The approach for performing cognitive health assessments comparesERP/EEG data to other assessments (behavioural (always), neuro-imaging(when available)) to verify or dispute the finding and how they arerelevant to the patient's functional consequences as a result of anacquired brain injury.

Each battery of tests by the testing machine has a cognitive functiontest, according to some embodiments. An objective, sensory-basedassessment tool is described herein, automatically controlling stimuluspresentation and tracking. A potential benefit for patients in aclinical setting is that the objective, sensory-based assessment tooldoes not suffer from the “noise” caused by a patient's emotions andfeelings (e.g., fears of a clinical environment and paranoia can, forother tests, lead to a false positive reading, for example, due to WhiteCoat Syndrome). Other “noise” that may be present in subjective tests inrelation to jaw clenching, and blinking, may also be accounted for.

ERP measurements that progress in complexity allow a holistic overviewof the brains overall function, not just one specific indicator, asperformed by alternate EEG based assessments that rely on, for example,the P300 alone. Through this method, the assessment is able to indicatethe severity of the functional consequences of injury as well asidentify functional regions of the brain that are affected.

In ERP waveforms, the system assesses, among others:

-   -   Response distribution: what areas of the brain activate or        “light up” when the stimuli are presented    -   Response amplitude: strength and direction of response (positive        or negative)—This is clinically relevant to identify what        regions of the brain have had a functional impact due to the        injury (as compared to either baselines or age/sex matched        controls).    -   Response latency: length of delay when a stimulus is presented        to when the brain responds. Similar to the amplitude, with high        amplitudes being healthier and low being unhealthy, responses to        tests should occur at (for example) 100, 200, 300 or 400        milliseconds dependent on what the stimulus is testing. If a        latency is shown (delay in response) it is indicative of damage.        The longer the delay, the more severe the damage.    -   Sensory Measurements: Physical Parameters    -   Mapping rest periods while the brain is not receiving any test        stimuli. (Coma and unresponsive wakefulness syndrome (vegetative        state)).        -   Mapping rest periods while the brain is not receiving any            test stimuli. (Coma and unresponsive wakefulness syndrome            (vegetative state) specifically). Useful to compare brain            activity at rest to active testing, specifically for Coma            and UWS patients as their level of ERP responses could be            much lower than that of a concussion patient. This helps to            show any minute indication of response to the testing as it            will differ from what is recorded at this resting state.    -   N1+P2: base mental function responses to a repeated tone        presentation (termed N100/P200 complex) indicative of auditory        and visual processing capabilities. Both responses are generated        bilaterally in the auditory cortices indicating the ability to        respond to auditory stimuli and confirming ability to engage in        testing without complications of core sensory issues.        -   N1+P2s are the earliest ERP responses measured. Viewable in            any paradigm. N1s occur when a patient receives a stimulus;            the N1 should always be closely followed by a P2.        -   Response amplitudes and latencies are unique to each            patient. The size of the amplitude and latency is also            dependent on the type of stimulus presented, e.g., A loud            versus a soft tone. The amplitude will be higher, and            latency will be earlier with louder versus soft tones.    -   MMN:        -   The MMN response, indicative of a patient's Automatic            Attention, measures their ability to respond to            environmental changes that are not actively attended.        -   MMN (negative):            -   Amplitude varies dependent on the stimuli presented and                varies across age spans. Best compared to age/sex                matched controls for ‘normal’ level. Closer to zero is                irregular.            -   Should occur around 200 milliseconds, shortly before the                P300. If before or after, irregular.    -   Cognitive Measurements: Information Processing    -   P300: A complex response that indicates multiple cognitive        functions depending on the paradigm.        -   P3a: Indicative of Reactive Attention—the brain's ability to            respond to stimulus deviance. The P3a is associated with            brain activity related to the engagement of attention            (especially orienting and involuntary shifts to changes in            the environment) and the processing of novelty. For example,            in a series of auditory tones, if the sound of a louder tone            is inserted into a series of less-loud auditory tones, the            neural response to that louder tone would contain a P3a.            Accordingly, this assessment utilizes the P3a to measure            “reactive” attention.        -   P3b: Indicative of Concentration—the mental effort of            focusing one's attention on a task. The P3b response is            affected by an individual's ability to distinguish            rarely-occurring stimuli (called “deviants”) from            frequently-occurring stimuli (called “standards”) in a            stimulus sequence. The stimuli must be related to the task            in some way. For example, in a series of tones where there            are different infrequent “deviant” tones, a P3b will be            elicited to the infrequent stimuli when the patient is asked            to attend to and differentially respond (i.e., mouse click)            to the “standard” and “deviant” tones.        -   P3b: Indicative of Working Memory—a cognitive system            responsible for temporarily holding information available            for processing. The P3b measures working memory presence and            efficiency. The response is obtained in response to complex            visual pattern stimuli, some of which are repeated            throughout the sequence. The client's task requires            maintaining working memory templates of what stimuli are and            are not repeated and responding (mouse click) accordingly.            The P3b reflecting working memory differs from other P3b            responses because it occurs at a later latency and has a            different voltage distribution across the scalp, indicating            a different group of neural generators producing the            response.        -   P300 (positive): Amplitude varies dependent on the stimuli            presented and varies across age spans. Best compared to            age/sex matched controls for ‘normal’ level. Closer to zero            is irregular.        -   Should occur at 300 milliseconds. If before or after,            irregular.    -   N400: Complex Language Comprehension response that reflects        language comprehension integrity—indicative of a patient's        ability to process word and phrase meanings, sentence grammar,        and discourse.        -   N400 (negative): Amplitude varies dependent on the stimuli            presented and varies across age spans. Best compared to            age/sex matched controls for ‘normal’ level. Closer to zero            is irregular.        -   Should occur at 400 milliseconds. If before or after,            irregular.    -   N2b: Indicative of Executive Functions—a collection of        interacting processes that represent a set of skills that all        work to make it possible for an individual to make plans,        anticipate consequences of behaviour, organize schedules and        generally function competently in life and society. The        complexity of executive functions means that the skill sets        involved are often represented by a range of ERP components with        the most notable being the N2b. The N2b reflects focused        attention and concentration that enables monitoring of one's own        behaviour in order to inhibit a response to one type of stimulus        while continuing to respond to another type of stimulus.        -   N2b reflects an indicator of the cognitive network comprised            of multiple processes that together enable complex behavior.        -   N2b (negative): Amplitude varies dependent on the stimuli            presented and varies across age spans. Best compared to            age/sex matched controls for ‘normal’ level. Closer to zero            is irregular.        -   Should occur at 200 milliseconds.

A complex cognitive function test such as testing Language Comprehensionrequires a patient's active attention as it is a conscious choice tolisten to what a person is communicating, and memory to 1) retain whatwas said to be able to respond, and 2) pull from memory to identifylanguage and understand its meaning.

The definitions and the outcomes of each ERP responses (generated in theCognitive Function Tests) are most easily clinically relatable toneuropsychologists as the data matches testing that they would otherwisecomplete offline with manual measurements, which are inherentlysubjective. The testing validates the outcomes of these tests andcaptures the responses as purely objective data, extracting anypotential for interpretation.

Although the specific ERP responses are outside the existing measuresoccupational therapists (OTs), physical therapists (PTs) and speechlanguage pathologists (SLPs) utilize (today), reports generated by thesystem described herein are also extremely clinically valuable to thesetypes of therapeutic interventions in addition to neuropsychology asthey: track progress, and with repeated assessments throughoutrehabilitation, the reports provide a tool for tracking a patient'sprogress, and aids in confirming effective methods of treatment for theinjury and for the patient.

Functional improvements are measurable approximately 30 days beforethose improvements translate to behaviour, which provides earlyencouragement and verification that the chosen methods of rehabilitationare working.

The reports also help set recovery expectations, whereby the reportshelp the clinician, patient and family understand the length of time,and level of involvement required in a rehabilitation plan. The reportsalso provide benchmarks of the patient's ‘norm’ against age/sex matchedcontrols and their personal benchmarks (when available) to confirm ifthe patient is ready to return to activities or return to regularactivity or play without any uncertainty, for concussed patientsspecifically.

The reports, for example, help a clinician indicate if the patient has aconcussion or ABI, and what type it is (e.g., differentiating betweencoma and/or unresponsive wakefulness syndrome (vegetative state), and ifa concussion is present), the severity of that concussion or ABI, theareas of function that are impacted.

Reports generated may include, for example:

-   -   Patient reports for specific conscious state: 1) coma, 2)        unresponsive wakefulness syndrome (vegetative state), and 3)        concussion.    -   Reports formatted specifically for clinical rehabilitation and        personal injury law firms, among others.

In some embodiments, the reports, in data structure formats, are adaptedfor conducting downstream machine learning (e.g., supervised learning)based on training, validation, and test pairs of data features andoutcomes. Accordingly, over time, the weights may be modified to overalltrack towards an improved accuracy (e.g., sensitivity and/orspecificity).

FIGS. 4, 5A and 5B, and 5C are directed to patients who are comatose orexperiencing unresponsive wakefulness syndrome (vegetative state).Example cognitive function tests include;

-   -   1. MMN    -   2. P300    -   3. N400+PMN    -   4. Resting State

FIG. 6 is directed to patients who are suspected of having concussions.Example cognitive function tests include;

-   -   1. P300    -   2. N400    -   3. MMN    -   4. N2b/P3b    -   N1+P2s are measured within each paradigm

Referring back to FIG. 4 , FIG. 4 is a flow diagram depicting an examplemethod for a cognitive health assessment tailored for patients who arecomatose, according to some embodiments.

A baseline set of behavioural tests are conducted to compare results tothe EEG/ERP cognitive function test outcomes illustrative of theimproved approaches described in some embodiments.

An example of behavioural tests used is the Glasgow Coma Scale (GCS).The Glasgow Coma Scale (GCS) can be utilized as it is one of the mostcommonly practiced Coma Scales. The Coma Scale measures basic observablebehavioural biological function (eye, verbal, motor responses) in apatient. The GCS measures different levels of function such as eyeopening, verbal response and motor response. Generally, the scoring fromlow to high reflects poor to good current state, which in turn reflectspoor to good prognostics (outcome).

Cognitive function tests are then conducted using EEG, having thefollowing approaches, according to some embodiments:

Sequence 1: Automatic Attention (Measure Mismatch Negativity (MMN))

-   -   The system begins playing consistent auditory stimuli to the        patient with occasional outlier tones without any instruction to        listen for specific tones or changes.

Sequence 2: Reactive Attention, Concentration, Working Memory(P300s+N2b)

-   -   Progressing from MMN, the system presents a screen to the        patient (or generates audible instructions), “You are going to        hear this tone again, count its occurrences if you can. You are        also going to hear a new tone (deviant) that is different than        this tone you've heard. Pay attention to this.” The patient now        has the instruction to identify the differences between tones.        The purpose of the test is to look for a more complex response.    -   While playing the standard tone intermixed with the periodic        deviant, the system will monitor for an N2b, which has similar        timing to the MMN but occurs when a patient is paying attention.        The N2b occurs at the same time as an MMN, and usually has a        larger amplitude. This will be assessed in comparison to the        P300.    -   Deviant examples are: an alternate tone, stating the patient's        name, stating a random word e.g., “Tree”, a recognizable sound        such as a dog bark/phone ringing.    -   Each deviant stimulus (e.g., tone, name, random words, random        noises) should garner a slightly different, uniquely measurable        P300 response. For example, a patient's P300 response should be        slightly different than their response to any other deviant        sound, as showing recognition of the patients' name can provide        a strong indication of consciousness.

Sequence 3: Language Comprehension (N400s) and Phonological MappingNegativity (PMN)

-   -   Basic (nonsensical sentences): Tests language        processing+vocabulary knowledge        -   The system will output a series of sentences to the patient            such as, “The pizza was too hot to eat”, “The pizza was too            hot to sing”.        -   The patient will show an N400, if they are in a conscious            state, to the word “sing” in the example sentences because            it is not a verb that makes sense within this sentence.    -   Complex (doze sentences): Tests semantic prediction        -   The system is controlled to output sentences with subtle            inaccuracies such as, “The pigs wallowed in the pen” vs “The            pigs wallowed in the mud”. If a patient is showing N400            responses, and complex sentences are recognized, it shows            the level of intact language receptive abilities a patient            is displaying, for example, high level of language function.    -   Phonological Mapping Negativity (PMN)        -   The PMN is a language related ERP component that occurs in            response to phonological processing of speech. While it is            independent of the semantically related N400, it often            occurs with it during tests that manipulate how unexpected a            word is within speech. That is, it responds to violations of            expectations so that it will occur if a patient expects a            specific word, but an alternate is in its place. E.g., The            patient may expect to hear the word “mouse” in the sentence            “she chased the mouse with a broom”, but instead heard            “lampshade”.

Sequence 4: Resting State

-   -   Brain activity is mapped when no stimuli are present with the        intent of measuring frequency and power characteristics of the        patient's EEG.

Within Sequences: Measure N1+P2s

-   -   N1+P2s are measured within each paradigm. Responses prior to        MMN, P300, N400 or PMN. The responses are indicative of auditory        function. If N1 and P2s are not detected, auditory brain stem        responses and middle latency responses are further examined to        check the integrity of the auditory pathway systems.

FIG. 5A, FIG. 5B and FIG. 5C are flow diagrams depicting an examplemethod for a cognitive health assessment tailored for unresponsivewakefulness syndrome (UWS) (vegetative state) patients (newpatients/existing patients), according to some embodiments.

Due to the complexity of unresponsive wakefulness syndrome (vegetativestate), follow up assessments have been outlined in addition to theinitial assessment to summarize how testing can be customized topinpoint functional abilities in a patient who is not able tocommunicate with other methods (speech, writing, signing) other thanthrough the brain measures/ERPs the system records.

Similar to FIG. 4 , behavioural tests are conducted (e.g., Coma RecoveryScale—Revised analysis to track patients who have emerged from comas).

For unresponsive wakefulness syndrome (vegetative state) patients,cognitive function tests are conducted using EEG, having the followingapproaches, according to some embodiments:

-   -   INITIAL ASSESSMENT        -   Step 1: If the system tested the patient in coma, follow the            same process as Coma Cognitive Health Assessment to assess            changes in function. If the system did not test the patient            in Coma, follow the same process as Coma Cognitive Health            Assessment to create an initial report.    -   FOLLOW UP ASSESSMENT/S        -   Step 1a: (For patients whose initial assessment occurred            while diagnosed as being in an UWS state)        -   Re-administer initial assessment to check for progress and            assess if ready to proceed with additional testing.        -   Step 1b: (For patients whose follow up assessment after Coma            emergence while in an UWS state/For UWS patients who have            shown progress in follow up assessments)        -   Consult with family/friends of the patient with a            standardized questionnaire Questions examples include:        -   Who are some of patient X's favourite celebrities?            (Political figures, athletes, actors, etc.)        -   What is their favourite band/musician/genre?        -   What are some of their favourite topics of conversation?        -   Describe their day-to-day life—locations, interactions with            people, commute routes etc.        -   Step 2: Customize section of assessment with family/friends            involvement to test for voice recognition. E.g., Have            family/friends record a sequence of names that includes the            patient's.        -   Step 3: Tailor assessment to create stimuli that will work            for the patient's age, musical preferences, famous faces,            familiarities of environment as indicated from the interview            in Step 1b.        -   Behavioural tests are conducted (e.g., Coma Recovery            Scale—Revised analysis).    -   FOLLOW UP ASSESSMENT CONTINUED        -   N400 sequences: Complex Language Comprehension with patient            customized tests. Measuring N400s—Negative response at 400            milliseconds in a healthy person.        -   Sequence 1: Complex voluntary attention, meta-linguistic            capabilities, language comprehension (recognizing            abnormalities in sentences)        -   This sequence is used to identify if a patient can recognize            if the semantics of a sentence do not make sense, as well as            their knowledge of how things function in the real world. If            a patient can identify that “socks do not go with coffee”            (for example), the system scales the subtlety of the            sentences from less subtle to more subtle to assess what the            extent of the patient's comprehension abilities. For            example, for more subtlety, the system will add sentences            that are not semantically incongruous, but instead have            subtle irregularities.        -   Basic (nonsensical sentences): Tests language processing            +vocabulary knowledge to ensure patient stability from            initial UWS assessment.            -   The system will output a series of sentences to the                patient such as, “The pizza was too hot to eat”, “The                pizza was too hot to sing”.        -   The patient will show an N400, if they are in a conscious            state, to the word “sing” in the example sentences because            it is not a verb that makes sense within this sentence.    -   Complex (doze sentences): Tests semantic prediction        -   The system is controlled to output sentences with subtle            inaccuracies such as, “The pigs wallowed in the pen” vs “The            pigs wallowed in the mud”. If a patient is showing N400            responses, and complex sentences are recognized, it shows            the level of intact language receptive abilities a patient            is displaying, for example, high level of language function.            These tests provide additional layer of complexity from            initial assessment: as baseline doze sentences required the            patient to identify when a word in the sentence isn't quite            right. This added level will leave a word out of a sentence,            e.g., “He stopped to talk to her about the . . . ”.        -   The patient should show a response to the missing word due            to it being unfinished. The end of the sentence is not            obvious, so the N400 should be a strong response as the            patient should wonder what is missing from the sentence.            Nothing is ‘wrong’ with these unfinished sentences. They are            used to measure how well a patient can predict how the            sentence will end (semantic prediction).    -   Sequence 2: Vocabulary Knowledge (in isolation as opposed to        sentences in Sequence 1 basic and advanced N400 testing)    -   To test to see if a patient has lost large portions of their        vocabulary, for example, the Peabody Picture Vocabulary Test can        be used to test patients who have their eyes open. For example,        the system will show a picture of a cat and will present an        accurate word match for the picture (cat) or, will present an        inaccurate word match for the picture (telephone). N400s and        proceeding responses are examined, responses should be large        when a picture and word do not match. If a patient is not        showing N400 responses for these mismatches, it explains they        are not responding well to language because they have lost some        elements of vocabulary knowledge. Vocabulary Knowledge tests are        a pinnacle of conscious measurements because they require the        complex levels of cognitive function. Vocabulary used is of        varying sophistication.        -   P300s & related component sequences: Memory Tests utilize            industry standard methods (such as “Famous Faces”,            “Continuous Visual Memory Test”, and “Digit Span”—some            examples but can be interchanged) that have also been            adapted for computer presentation to record ERPs,            integrating the family/friend interview responses to be most            relevant to the patient and what they were familiar with            prior to injury.        -   Sequence 1: Visual Memory (requires vision) Facial            recognition leverages test called “Famous Faces”. From the            consultation with the family/friends, the test is geared to            the patient's age group, and ensured to include faces they            will recognize, and ones that they have not seen (non-famous            faces). When a patient sees a face they have seen before in            life, they will show a P300 brain response. If they do not            show this response, the system detects the patient does not            have memory for faces. In addition, the test is further            customized by including images provided by the family of the            patient's family members and friends.        -   Sequence 2: Complex Memory—Shape recognition leverages the            “Continuous Visual Memory Test” (CVMT). Testing shape            recognition is valuable for patients with a language            barrier. It is used to determine if the cognitive barrier is            due to language or memory alone. The system shows the            patient geometric shapes and will periodically repeat            several of them, asking the patient to mentally identify            when they see one repeated. This will indicate the integrity            of their non-verbal visual memory.        -   Sequence 3: Short Term Memory: The “Digit Span” test is used            to examine short-term memory. The system will show on a            screen or audibly state (depending on the patient's            condition) 3 or more digits. When a digits sequence is            presented followed by an identical or different ordering of            the digits, if the patient recognizes an out-of-sequence            digit, they will show a P300 response.

FIG. 6A is a flow diagram 600A depicting an example method for acognitive health assessment tailored for patients with concussions,according to some embodiments.

Behavioural Tests: The system leverages industry standard behaviouralassessments such as self-reported batteries test results, a selectionare included as part of the assessment. These have been selected, asthey are the most popular existing behavioural methods of assessmentsused by rehab clinicians.

-   -   Like behavioural tests of the Coma and UWS cognitive health        assessments, these are included to bridge the gap between        current gold standards of concussion and ABI assessment, by        comparing their subjective results to the objective results of        the EEG portion of the cognitive health assessment. This        comparison confirms (or disproves) the validity of the        behavioural results. This is especially important for concussion        assessments as results are often purposefully inaccurately        reported by the patient to speed up their return to        activity/play, or conversely to be removed from work/activity or        provided accommodations for an injury. These specific methods        are very commonly used for athletes pre-season and during season        to track progress but have a huge margin for error due to them        being subjective and dependent on the patient's disclosure. By        comparing the claims to the EEG/ERP findings, it can confirm        accuracy or identify false claims.

Self-Reported Batteries

-   -   Symptoms are self-reported by the patient, most commonly on a        symptom scale and checklists (e.g., How bad are your headaches        on a scale of 1-10?).    -   The GAD7, PHQ9, SF-36, and PCSS self-report inventories are used        to evaluate the general health and well-being of the patient.        -   GAD7 evaluates anxiety        -   PHQ9 evaluates general health,        -   SF-36 evaluates general health,        -   PCSS evaluates post-concussion symptomatology.

Cognitive Function Tests

-   -   Sequence 1: P300s—Reactive Attention and Working Memory    -   PART 1: Tester instructs the patient that they will hear a        series of repeated tones. Each time that standard repeated tone        occurs, the patient is instructed to click left with the mouse.        At any point that the patient hears a deviant tone, they are        instructed to right click on the mouse. Throughout this test,        the patient is hearing the tones through headphones, and looking        at a white target on a black screen—this helps to have the        patient keep their head steady and reduce recorded ‘noise’ in        the EEG recordings. In some embodiments, the system generates        the instructions, alongside the tones and records the        measurements.    -   PART 2 (Counterbalance): The tester instructs the patient that        they will hear a series of repeated tones. This time, each time        a standard repeated tone occurs, the patient is instructed to        click right with the mouse. Each time a deviant tone appears the        patient is instructed to click left with the mouse. This is the        opposite of the first step in this paradigm.    -   Sequence 2: N400—Language Comprehension: The tester instructs        the patient to listen to a variety of sentences and pay        attention to whether they make sense. The patient is instructed        to click left to ones that make sense, and right to ones that do        not. Basic (nonsensical sentences): Tests language        processing+vocabulary knowledge        -   The system will present a series of sentences to the patient            such as, “The pizza was too hot to eat”, “The pizza was too            hot to sing”.        -   The patient will show an N400, if they are processing            language and maintaining attention throughout the sentence.            The response will be to the word “sing” in the example            sentences because it is not a verb that makes sense within            this sentence. In the Concussion Cognitive Health            Assessment, the responses are examined more specifically for            their strength and latency than in the coma and UWS            assessments, that are looking for presence versus absence of            response.    -   Complex (doze sentences): Tests semantic prediction        -   The system is controlled to output sentences with subtle            inaccuracies such as, “The pigs wallowed in the pen” vs “The            pigs wallowed in the mud”. If a patient is showing N400            responses, and complex sentences are recognized, it shows            the level of intact language receptive abilities a patient            is displaying, for example, high level of language function.

Sequence 3: Automatic Attention (MMN)—The tester instructs the patientto watch a video without its regular audio track. They will hear beepsthroughout the clip but have not been instructed to listen for them.

Sequence 4: Working Memory (P3b)

-   -   A cognitive system responsible for temporarily holding        information available for processing. The P3b measures working        memory presence and efficiency. The response is obtained in        response to complex visual pattern stimuli, some of which are        repeated throughout the sequence. The client's task requires        maintaining working memory templates of what stimuli are and are        not repeated and responding (mouse click) accordingly. The P3b        reflecting working memory differs from other P3b responses        because it occurs at a later latency and has a different voltage        distribution across the scalp, indicating a different group of        neural generators producing the response.

Sequence 5: Executive Function (N2b/P3b)

-   -   The Executive Function N2b and P3b responses indicate a        cognitive network comprised of multiple processes that together        enable complex behaviour.    -   N2b/P3b measures the collection of interacting processes that        represent a set of skills that all work to make it possible for        an individual to make plans, anticipate consequences of        behaviour, organize schedules and generally function competently        in life and society. The complexity of executive functions means        that the skill sets involved are often represented by a range of        ERP components with the most notable being the N2b. The N2b        reflects focused attention and concentration that enables        monitoring of one's own behaviour in order to inhibit a response        to one type of stimulus while continuing to respond to another        type of stimulus.    -   The patients will listen to or watch complex visual or auditory        pattern stimuli, some of which are repeated throughout the        sequence while the patient has been instructed to actively        ignore specific visuals or auditory tones or patterns, and        recognize or respond to alternate repeated visuals or auditory        tones or patterns.

Within Sequences: Measure N1+P2s

-   -   N1+P2s are measured within each paradigm. Responses prior to        MMN, P300, N400 or PMN. The responses are indicative of auditory        function. If N1 and P2s are not detected, auditory brain stem        responses and middle latency responses are further examined to        check the integrity of the auditory pathway systems.

FIG. 6B is a flow diagram 600B depicting an example method for acognitive health assessment tailored for clients seeking a generalcognitive health assessment, according to some embodiments.

The tests may also be utilized for conducting mental competencyassessments, elderly competency assessments, neurodevelopmental disordercompetency assessments, measuring drug effects on brain function, andgeneral brain health tracking.

FIG. 7A, FIG. 7B, FIG. 7C, and FIG. 7D show example brain sensorwaveform diagrams, according to some embodiments.

FIG. 7A includes the example graph 700A, showing example auditory N1/P2responses as well as example MMN responses showing amplitude chartedagainst time.

FIG. 7B includes the example graph 700B, showing example visual N1/P2responses as well as example P3b responses showing amplitude chartedagainst time.

FIG. 7C includes the example graph 700C, showing example N2b, P3a, andP3b responses showing amplitude charted against time.

FIG. 7D includes the example graph 700D, showing an example N4 responseshowing amplitude charted against time.

FIG. 8A, FIG. 8B, and FIG. 8C are example interface screens comparingbrainwave patterns of a patient compared to a concussed control group.,according to some embodiments. FIGS. 8A-8C illustrates topographicalmapping generated throughout waveform data collection. Areas of thebrain ‘light up’ at the time of an ERP response. Although one may seeirregular responses in waveforms, one may not see irregularities on thetopographical maps or vice versa. Therefore, topographical maps confirmthe elicitation of an ERP.

FIGS. 8A-C shows grand-averaged P300 protocol waveforms and theirrespective scalp distributions recorded at Cz, evoked by target stimuli,for each group (Controls Left, Concussed person, Right). (A) 800A: N1,N2b, P3a, and P3b components evoked in the Frequency condition. (B)800B: N1, N2b, P3a, and P3b components evoked in the Duration condition.(C) 800C: N1, N2b, P3a, and P3b components evoked in the Intensitycondition.

Three different deviants (frequency/pitch, duration, intensity/loudness)were presented to different populations, as the different populationsseem to respond differently to the different deviants:

-   -   P3as are fronto central and positive—darker grey regions.    -   P3bs are centro-parietal and positive—darker grey regions.

Examining the waveforms for the Controls, a clear N1 response is seen tostimulus onset with a typical frontocentral distribution (FIG. 8A, 8B,8C); similar characteristics are observed in the concussed group. Thefollowing N2b component exhibits a characteristic central distributionwith minor representation at frontal sites (FIG. 8A, 8B, 8C).

These waveform morphological features are also seen in the concussedgroup, although the N2b has increased frontal representation in theconcussed group that is seen in response to Duration (FIG. 8B) andIntensity (FIG. 8C) deviants, in particular.

However, the comparative topographical maps for the P300 exhibit cleardifferences in the development and distribution of the P300 in responseto each deviant stimulus type. In controls, the P3a element of the P300exhibits a frontal distribution that extends in an anterior-posteriormanner as far back as the occipito-parietal sites for Frequency andIntensity deviants (FIG. 8A, 8C) but shows only a frontocentraldistribution for Duration deviants (FIG. 8B).

These distributional effects suggest a combinatorial P3a and b in thiswaveform. The topographical maps for the concussed group show a similaranterior-posterior distribution; however, a fairly striking left-sidedabsence of a response resulting in an unusual right asymmetry of theresponse is apparent across all types of deviants (FIG. 8A, 8B, 8C). TheP3b occurring quite late for both Controls and concussed groups exhibitsa parietal distribution that is apparent and similar in both groups.

The most striking feature of these waveforms is the near 50% reductionin P300 amplitude (both P3a and P3b) in the concussed group across allconditions (FIGS. 8A-8C) compared to Controls and the smaller but stillnotably reduced N2b amplitude again in the concussed group.

Statistical analysis provided confirmatory support for observations(Table 1, below). Group differences were not observed for either thelatency or amplitude of the N1. However, N2b amplitudes proved to besignificantly smaller in the concussed group compared to the controlsample (F(1, 35)=5.08, P<0.05).

Additionally, there was an interaction of Group X Condition for the N2bamplitudes (F (2, 70)=4.91, P<0.05) that post hoc analysis revealed wasattributable to the much smaller amplitudes to Duration deviants in theconcussed group compared to Controls (F (1, 35)=14.38, P<0.01). Therewas a main effect of Group such that the P3a amplitudes in the concussedgroup were significantly smaller than those exhibited in the Controlsample (F (1, 35)=6.34, P<0.05). In addition, delayed response latencieswere found for the P3b in the concussed group compared to Controls (F(1, 35)=15.32, P<0.01). Lastly, it was found that a main effect of groupon P3b amplitude (F (1, 35)=8.08, P<0.01) where the concussed groupexhibited a reduction in P3b amplitude compared to healthy controlparticipants.

The abnormalities found in two different levels of attention asmanifested by the P300 and MMN in this example indicate potential issuesrelated to concussions. Increased latencies of the P300 may be areflection of greater difficulties in allocating attentional resourcesfor memory processing, and latency delays of the P3b component can beinterpreted as indicative of slower cognitive processing speeds. The N2bis sensitive to stimulus deviance from an on-going sequence only whenstimuli are being attended to; a characteristic demonstrating that theN2b requires and reflects conscious attention.

The decrease in N2b amplitude may reflect a deficit in the processingcapacity of information contained in a stimulus. With the addition ofthe MMN protocol, the system adds a level of understanding to thecognitive consequences of concussions. As noted above, the MMN isassociated with a level of “pre-attentive” processing that is elicitedindependently of conscious attention while still requiring theindividual to be in a conscious state in addition, exclusively withinthe MMN protocol, the study results noted above demonstrated asignificant reduction in N1 amplitude. The N1 is a pre-attentive ERPlinked to the auditory cortex that has been found to be sensitive toloudness, frequency, and sound onset. The significant decrease in N1amplitude may suggest difficulties in auditory processing.

FIG. 9 shows a graphical user interface 900 including example resultsfrom a cognitive health assessment report.

The patient's complex function EEG data is illustrated on a PercentileValue Scale. The graphical results present a complete view of apatient's performance across complex cognitive function categories thatcan be compared numerically to healthy controls. Each cognitive functionis presented with a unique color and reports the neurophysiologicalperformance of the patient as detected in response time (leftsemicircle) and response strength (right semicircle).

Percentile Value Scale: Each patient is compared to a reference databaseof neurophysiological brain responses. A numeric value is used torepresent the patient responses relative to healthy controls on a scalefrom 1 to 100.

-   -   There can be three categories of performance, for example:        -   75-100: indicates a NORMAL response        -   25-75: indicates a MODERATE DEFICIENCY        -   below 25: indicates a SEVERE DEFICIENCY

Once EEG data is collected during a patient's cognitive healthassessment, it is statistically analyzed through the system's analysissoftware.

An example of the steps involved include the following: The 64 electrodescalp positions on the head are divided into 20 segregated Regions ofInterest (ROIs), with 3 to 6 electrodes per region.

Regions are created by clustering electrodes from left (L), midline (M),and right (R) positions with frontal (F), central (C), and parietal (P)positions. Of those 20 ROIs, 9 are selected and subsequently groupedinto 3 independent scalp sectors: Frontal (R-F, M-F, L-F), Central (R-C,M-C, L-C), and Parietal (R-P, M-P, L-P).

Statistical analyses are performed for both amplitude and latency usingmixed-effects analysis of variance (ANOVAs) with an alpha level ofP<0.05. Degrees of freedom are corrected using the more conservativeGreenhouse-Geisser estimates of epsilon to ensure avoidance of Type 1errors. EEG analyses are conducted on the peak amplitude (defined as theaverage amplitude within a time-window of −50 ms to +50 ms around thedetected peak) and latency (defined from stimulus onset to the detectedpeak) of ERP components for each condition (Standard, Frequency, andDuration) within ROIs where these specific components are found to bemaximal.

These statistical analyses are utilized to determine differences inamplitude and latency of different ERP components of each test withinthe cognitive health assessment. These statistical analyses are utilizedto determine differences in amplitude and latency of different ERPcomponents of each test between an individual patient and controlgroups.

These results are then processed by an algorithm in the software tochange the numerical representations into the Percentile Value Scale asshown on the Complex Functions Response Graph. Initial and follow-upassessment comparative analyses are conducted independently and in thecase of the follow-up assessment, then further compared to trackdifferences in ERP responses between the two (or more) test sessions totrack a patient's cognitive progression.

Table 1, below, is a non-limiting example reference illustrating betweengroup differences of amplitude and latency for the N1 and MMN within theMMN Protocol, as well as the N1, N2b, P3a, and P3b within the P300Protocol after Greenhouse—Geisser corrections for multiple comparisonswere applied. Other values are possible in respect of differentdemographics, and the values below are shown for illustration only.

TABLE 1 MMN Protocol P300 Protocol N1 Amplitude N1 Amplitude P3aAmplitude Effect df F P Effect df F P Effect df F P Group 35 5.74 <0.05*Group 35 1.88 >0.05 Group 35 6.34  <0.05* Group: Condition 1052.19 >0.05 Group: Condition 105 105 >0.05 Group: Condition 70 0.16 >0.05Group: Region 280 2.96 >0.05 Group: Region 280 280 >0.05 Group: Region280 1.15 >0.05 N1 Latency N1 Latency P3a Latency Effect df F P Effect dfF P Effect df F P Group 35 0.8 >0.05 Group 35 0.12 >0.05 Group 351.24 >0.05 Group: Condition 105 0.79 >0.05 Group: Condition 1050.25 >0.05 Group: Condition 70 1.01 >0.05 Group: Region 280 0.26 >0.05Group: Region 280 0.83 >0.05 Group: Region 280 0.92 >0.05 MMN AmplitudeN2b Amplitude P3b Amplitude Effect df F P Effect df F P Effect df F PGroup 35 10.01 <0.01** Group 35 5.08 <0.05* Group 35 8.08  <0.01**Group: Condition 70 5.98 <0.01** Group: Condition 70 4.91 <0.05* Group:Condition 70 1.06 >0.05 Group: Region 280 1.59 >0.05 Group: Region 2800.42 >0.05 Group: Region 280 1.43 >0.05 MMN Latency N2b Latency P3bLatency Effect df F P Effect df F P Effect df F P Group 35 0.85 >0.05Group 35 0.75 >0.05 Group 35 15.32  <0.01** Group: Condition 701.24 >0.05 Group: Condition 70 0.1 >0.05 Group: Condition 70 3.28 >0.05Group: Region 280 0.42 >0.05 Group: Region 280 0.85 >0.05 Group: Region280 0.22 >0.05 Note: “:” denotes an Interaction. *Indicates SignificanceBetween Groups <0.05. **Indicates Significance Between Groups <0.01.

The differences in the amplitude and latency of different ERP componentsof each test between an individual patient and control groups are usedto establish a graphical visual element size/position factor that isbased on the percentile value scale. The numeric value that was used torepresent the patient responses relative to healthy controls on a scalefrom 1 to 100 is used to modify the sizing of a visual elementrepresenting a normalized score for a particular category.

In some embodiments, the visual element is a slice of an exploded piechart (e.g., the chart shown on the left). The factoring can be based ona radius of the graphical representation, modifying the radius based ona percentage from 0-100%—for example, executive function at 100 has afull radius, while the other complex functions being measured havereduced radii. Other visual characteristics may also be controlled, suchas the color, opacity, tint, saturation, of the visual element. Inanother embodiment, the visual element is a bar chart. In someembodiments, to reduce an overall computational load by the devicedisplaying the graphical representation, the data structure transmittedto the device includes pre-generated size factor scores, generated by abackend server having more computational resources.

FIG. 10 is a diagram showing an example recovery progress tracker 1000from a cognitive health assessment report through repeat testing. Adifferent type of visual element is presented that shows changes over aperiod of time.

FIG. 11 is a block schematic of an example computing device 1100,according to some embodiments. The components shown are electroniccomputing components and can include a combination of hardware andsoftware. Electrical circuits are specially programmed usingmachine-interpretable instruction sets and the schematics shown arenon-limiting examples. For example, other, alternate, or differentcomponents are possible.

The device 1100 includes an interface unit 1102, which receives one ormore data streams from the sensor apparatus and controls datatransmissions to the stimulus presentment device, or downstreamcomputing devices, such as medical device controllers, electronic healthrecord data storages, and display controllers that control one or moregraphical user interfaces (e.g., a report engine). The interface unit1102 can also receive various commands from an operator, such asinitiate assessment, an indication of type of individual being assessed,among others.

The processor 1104 can include microprocessors, systems on a chip, andfield programmable gate arrays. The processor 1104 executes machineinterpretable instruction sets which configure the processor to performsteps of a method for health assessment, and in some embodiments, themethod further includes medical device control, display rendering of agraphical user interface, or appending data to electronic health recordstorage elements. The processor 1104 can include a central processingunit and can be an integrated circuit, and can include computingelements, such as a digital clock, one or more data registers, amongothers.

The communication interface 1106 is interoperable with a network toreceive, transmit and process data packets through the network. Thecommunication interface 1106 can include a wireless connection, acellular connection, a satellite connection, or a wired connection.

The processor 1104 includes instruction sets which include computerprograms and software which are directed to process the data sets usinga waveform feature extractor engine 1108. Steps of the assessment aregenerated by stimulus presentment controller 1110, and provided asinstruction sets for controlling the stimulus presentment device. Asdescribed in various embodiments, the stimulus presentment controller1110 controls the presentment of tactile, vibratory, audible, visible,and/or olfactory stimuli, which can include aberrant or deviant versionsof the same. The stimulus presentment controller 1110 is configured togenerate sustained stimuli overlaid with deviant stimuli, and in someembodiments, repetitive stimuli, depending on the particular brainwavepattern being tracked. The stimulus presentment controller 1110 can beconnected, for example, to a speaker and/or a display device. Thevarious types of stimuli, including standard and deviant versionsthereof, can be retrieved from a data storage 1150.

The difference engine 1112 is configured to generate metrics from thereceived signals, for example, by comparing the metrics againstdemographic controls from a population-level analysis, indicating, forexample, how the individual responds to various stimuli and whetherthere may be potential issues in relation to at least one of the metricsbeing evaluated. In some embodiments, the scalp region identificationengine 1114 segregates the signals received from the sensor apparatusand conducts the differencing analysis to assess scalp-region relateddifferences from the control, providing greater granularity andresolution to the metrics. For example, there may be reduced brainfunctionality or modified functionality isolated to specific brainregions as identified through the scalp positioning of the electrodes atvarious regions of interest.

The data message encapsulator 1116 is configured to generate instructionmessages which can be data structures including various fields forupdating electronic health records (e.g., encapsulated HL7 schema basedmessages) or JSON/XML, files that modify parameters of medical deviceoperation (e.g., modify a polling rate or an alert/alarm threshold, thispatient is potentially not comatose) or notification/alert generationthereof.

The rendering generator 1118 is configured to generate one or moregraphical user interface visual elements based on the determined metricsfrom the brain assessment techniques described in various embodimentsherein. The graphical user interface visual elements are encapsulated asdata message elements which can be transmitted to a display controllerand stored on a graphical user interface element storage. The graphicaluser interface can be invoked by the computing device (e.g., a doctor ora nurse's work station) such that the rendering can be generated as astatic image or as a dynamically generated set of user interfaceelements. As noted in embodiments herein, the encapsulated message caninclude metric data which can be transformed by the display controllerinto visual characteristic values for the user interface elements, or inother embodiments, the visual characteristic values are pre-processedand transmitted to the display controller (e.g., where the displaycontroller has limited computational functionality).

FIG. 12 is a method diagram 1200 of an example process, according tosome embodiments. In FIG. 12 , a method for generating data setsrepresentative of potential cognitive activity of a patient is shown.The steps shown are examples and alternate, different, less, more stepsare possible. For example, as noted in some embodiments, a partialbattery of tests may be suitable in certain situations.

The method operates on a computing system 1100 for generating data setsrepresentative of potential cognitive activity of a patient, thecomputing system including at least one processor and computer readablememory, the computing system comprising a sensor apparatus connected toone or more electrodes coupled to the patient's head, the one or moreelectrodes recording brainwave (EEG) data of the patient in respect of abrain of the patient; a stimulus presentation mechanism coupled to oneor more sensory output devices, the stimulus presentation mechanismgenerating a series of programmed stimuli to the patient while thesensor apparatus records the brainwave data of the patient as thepatient receives the series of programmed stimuli; and a waveformfeature extractor processing engine configured to process the brainwavedata of the patient to extract waveform features, the waveform featuresincluding at least one or more N1, P2, N400, MMN, P300 (P3a/P3b), andN2b responses.

The system uses a sensor apparatus connected to one or more electrodescoupled to the patient's head, the one or more electrodes recordingbrainwave (EEG) data of the patient in respect of a brain of thepatient, a portion of brainwave data of the patient during a firstresting period during which no stimulus is being presented to thepatient.

Stimuli, including repeated stimuli and deviant/modified stimuli, can besourced from a data storage element, or in some embodiments, transformedbased on the standard stimuli (e.g., modification of pitch or duration).

At 1202, the processor executes a process for controlling a stimuluspresentation mechanism to present a repeated auditory tone or visualimage presentation to the patient; and tracking, on a processorconfigured for monitoring data received from the sensor apparatus theone or more N1 and P2 responses to auditory tones or words to measurethe brain's processing of auditory stimuli or N1 and P2 responses tovisual stimuli to measure the brain's processing of visual stimuli.

At 1204, the processor executes a process for controlling the stimuluspresentation mechanism to present one or more auditory or visual phraseseach including one or more nonsensical, or otherwise inaccurate orunexpected portions of a sentence to the patient and tracking, by theprocessor: the one or more N400 responses recorded in one or morewaveform features during or proximate to the presentation of one or morenonsensical portions of a sentence to measure the brain's ability toprocess word and phrase meanings, sentence grammar and discourse.

At 1206, the processor executes a process for controlling the stimuluspresentation mechanism to present one or more incongruous, unexpected orotherwise surprising words or sentences within a language context; andtracking, by the processor the one or more N400 responses recorded inone or more waveform features during or proximate to the presentation ofone or more incongruous, unexpected or otherwise surprising words orsentences pairings to track the brain's ability to process word andphrase meanings, and vocabulary recognition.

At 1208, the processor executes a process for controlling the stimuluspresentation mechanism to present repeated tones or visuals intermixedwith deviant tones or visuals while presented in concert with a constanttone or visual; and tracking, by the processor:

the one or more MMN responses recorded in one or more waveform featuresduring or proximate to the presentation of the one or more deviant tonesor visuals to track the brain's ability to respond to environmentalchanges that are not actively attended.

At 1210, the processor executes a process for controlling the stimuluspresentation mechanism to present repeated tones or visuals intermixedwith deviant tones or visuals and tracking, by the processor: the one ormore P3a responses recorded in one or more waveform features during orproximate to the presentation of one or more deviant tones or visuals totrack the brain's ability to respond to stimulus deviance.

At 1212, the processor executes a process for controlling the stimuluspresentation mechanism to present repeated tones or visuals intermixedwith deviant tones or visuals while the patient has been instructed toactively recognize or respond to the deviant tones or visuals andtracking, by the processor: the one or more P3b responses recorded inone or more waveform features during or proximate to the presentation ofone or more deviant tones or visuals to track the brain's ability tofocus one's attention on a task.

At 1214, the processor executes a process for controlling the stimuluspresentation mechanism to present complex visual or auditory patternstimuli, at least one of which are repeated throughout the sequencewhile the patient has been instructed to actively recognize or respondto the repeated visuals or auditory tones or patterns and tracking, bythe processor: the one or more P3b responses recorded in one or morewaveform features during or proximate to the presentation of one or morerepeated tones or visuals to track the brain's ability to temporarilyhold information available for processing.

At 1206, the processor executes a process for controlling the stimuluspresentation mechanism to present complex visual or auditory patternstimuli, some of which are repeated throughout the sequence while thepatient has been instructed to actively ignore specific visuals orauditory tones or patterns, and recognize or respond to alternaterepeated visuals or auditory tones or patterns and tracking, by theprocessor the one or more N2b responses recorded in one or more waveformfeatures during or proximate to the presentation of one or more repeatedtones or visuals and reaction to one or more repeated tones or visualsto track the brain's ability to work through complex processes to enablecomplex behaviour.

The processor is configured for processing the data sets to identify oneor more differences in the N1, P2, N400, MMN, P300 (P3a/P3b), N2bresponses recorded in the one or more waveform features during thepresentation of the repeated auditory tone or visual image presentationto the patient and during the presentation of the repeated auditory toneor visual image intermixed with the deviants, and the one or more P3bresponses recorded in one or more waveform features in response tocomplex visual pattern stimuli, some of which are repeated throughoutthe sequence.

The processor then generates a data set based on the extracted waveformfeatures, the data set including data fields corresponding to at leastone of an automatic attention metric based at least on the differencesin the one or more MMN responses, a reactive attention metric based atleast on the differences in the one or more P3a responses, aconcentration metric the differences in the one or more P3b responses, aworking memory metric based at least the differences in the one or moreP3a responses, or an executive function metric based at least on thedifferences in the one or more N2b responses.

In some embodiments, the tool is a standalone, special purpose machinethat is adapted for use in a clinical setting. The special purposemachine may have specialized software and hardware, such as optimizedintegrated circuits or field programmable gate arrays. For example, thetool may be provided on a medical cart, coupled to a patient (e.g.,following a concussion or a comatose patient), and the cognitive healthassessments are measured as stimuli are presented (e.g., sound tones,vibrations, visual stimuli), or between when stimuli are presented.These stimuli are presented even to patients who are otherwiseunresponsive (individuals with locked in syndrome, etc.). The toolincludes EEG hardware, computer processors, and software that arespecially configured in relation to performing the above tests. Stimuliand acquisition software are adapted based on the techniques describedabove, including statistical analysis and measurements, which responsiveto the measurements, are used in generating decision support interfaces.

It will be appreciated that components exemplified herein that executesinstructions that include or otherwise have access to computer readablemedia such as storage media, computer storage media, or data storagedevices (removable and/or non-removable) such as, for example, magneticdisks, optical disks, tape, among others. Computer storage mediaincludes volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information, suchas computer readable instructions, data structures, program modules, orother data.

Examples of computer storage media include RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks(DVD), blue-ray disks, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or media which can be used to store the desired information and whichcan be accessed by an application, module, or both. Computer storagemedia may be part of the mobile device, tracking module, object trackingapplication, etc., or accessible or connectable thereto. Applicationsherein described are implemented using computer readable/executableinstructions that may be stored or otherwise held by such computerreadable media.

Thus, alterations, modifications and variations can be affected to theparticular embodiments by those of skill in the art without departingfrom the scope of this disclosure.

In further aspects, the disclosure provides systems, devices, methods,and computer programming products, including non-transientmachine-readable instruction sets, for use in implementing such methodsand enabling the functionality described previously.

Although the disclosure has been described and illustrated in exemplaryforms with a certain degree of particularity, it is noted that thedescription and illustrations have been made by way of example only.Numerous changes in the details of construction and combination andarrangement of parts and steps may be made.

Except to the extent explicitly stated or inherent within the processesdescribed, including any optional steps or components thereof, norequired order, sequence, or combination is intended or implied. As willbe understood by those skilled in the relevant arts, with respect toboth processes and any systems, devices, etc., described herein, a widerange of variations is possible, and even advantageous, in variouscircumstances.

What is claimed is:
 1. A method for assessing cognitive health of apatient, the method comprising: performing a neurophysiologicalassessment of a cognitive health of a patient based on one or morebehavioral responses; obtaining electroencephalogram (EEG) data duringthe neurophysiological assessment; determine ERP data by identifying oneor more event-related potentials (ERPs) from the EEG data; and analyzingthe ERP data and comparing to an output of the neurophysiologicalassessment to verify or dispute a cognitive health finding of theneurophysiological assessment.
 2. The method of claim 1, whereinanalyzing the ERP data comprises performing one or more cognitivefunction tests to assess function of one or more cognitive attributesbased on the ERP data.
 3. The method of claim 1, wherein the ERP dataincludes P300 responses.
 4. The method of claim 1, wherein the ERP dataincludes P300 responses and are used to assess attention andconcentration of the patient by performing and analyzing an auditorymeasure mismatch negativity (MMN) test.
 5. The method of claim 1,wherein the ERP data is used to assess information processing functionof the patient by performing and analyzing an auditory MMN test.
 6. Themethod of claim 1, wherein the ERP data includes P300 responses and isused to assess working memory of the patient by performing and analyzinga continuous visual memory test (CVMT).
 7. The method of claim 2,wherein a cognitive function assessment based on the ERP data isstatistically compared to a control in healthy patients.
 8. The methodof claim 7, wherein the control is an age or sex based control.
 9. Themethod of claim 2, wherein a cognitive function assessment based on theERP data is compared to a baseline of the patient.
 10. The method ofclaim 1, wherein the method is performed any time post injury.
 11. Themethod of claim 10, wherein the method is performed beyond 72 hours postinjury.
 12. The method of claim 1, further comprising repeating thesteps in claim 1 at a subsequent time to track recovery or changes overtime.
 13. The method of claim 1, wherein the method is performed toassess any of: a brain injury, mental competency, elderly competency,neurodevelopmental disorder competency, brain damage, drug effects onbrain function, and general brain health tracking.
 14. The method ofclaim 1, further comprising: placing one or more external electrodes ofa sensor apparatus on a head of the patient, the one or more electrodesbeing attached to a sensor apparatus configured for recording the EEGdata from the brain of the patient.
 15. The method of claim 14, whereinthe method is performed after a severe head injury.
 16. The method ofclaim 1, wherein the method is performed in patients that areunresponsive due to sensory or perceptual impairments, aphasia, motorimpairments, subclinical seizure activity, pain, fluctuating, arousal,or fatigue.
 17. The method of claim 1, wherein obtaining the EEG data,analyzing of the ERP data and comparing to the output of theneurophysiological assessment is performed by a cognitive healthassessment system having a memory with programmable instructionsrecorded thereon, the instruction being configured for performing thesteps in claim
 1. 18. The method of claim 17, wherein the programmableinstructions are further configured to perform one or more cognitivefunction tests on the patient.
 19. The method of claim 18, wherein thecognitive health assessment system includes headphones worn by thepatient and the one or more cognitive function tests includes anauditory MMN test.
 20. A system for assessing cognitive health of apatient, the system comprising: a sensor apparatus connected to one ormore external electrodes for placement on a head of the patient, the oneor more electrodes configured for recording EEG data with respect to abrain of the patient; one or more sensory output devices configured forproviding sensory stimuli to the patient; a stimulus presentationmechanism coupled to the one or more sensory output devices, thestimulus presentation mechanism configured for generating a series ofprogrammed stimuli to the patient while the sensor apparatus records theEEG data of the patient as the patient receives the series of programmedstimuli, including stimuli associated with a neurophysiologicalassessment of a cognitive health of a patient based on one or morebehavioral responses; a waveform feature extractor configured to processthe EEG data of the patient to determine ERP data by extracting one ormore ERP responses to the series of programmed stimulus; and a processoroperably coupled to a memory having instructions recorded thereon thatare configured to: control the one or more sensory output devices toprovide the series of programmed stimuli to the patient to facilitatethe neurophysiological assessment, wherein the programmed stimulicomprises a stimuli pattern of intermixed and/or repeated sensorystimuli; record the EEG data triggered by the stimuli pattern, whereinthe EEG data includes one or more ERP responses; determine a cognitivehealth assessment of the patient based at least in part on an analysisof the ERP data and comparison to an output of the neurophysiologicalassessment.
 21. The system of claim 20, wherein the ERP data includesP300 responses.
 22. The system of claim 20, wherein the ERP dataincludes P300 responses and are used to assess attention andconcentration of the patient by performing and analyzing an auditory MMNtest.
 23. The system of claim 20, wherein the ERP data is used to assessinformation processing function of the patient by performing andanalyzing an auditory MMN test.
 24. The system of claim 20, wherein theERP data includes P300 response and is used to assess working memory ofthe patient by performing and analyzing a CVMT test.
 25. The system ofclaim 20, wherein the sensory stimuli include any of: auditory tones,vibrations, olfactory, or visual stimuli.
 26. A non-transitory machinereadable medium storing a program for controlling a cognitive healthassessment system comprising: a sensor apparatus connected to one ormore electrodes for placement on a head of the patient and configuredfor recording EEG data with respect to a brain of the patient during aneurophysiological assessment of a cognitive health of a patient basedon a behavioral response, one or more sensory output devices configuredfor providing sensory stimuli to the patient, a stimulus presentationmechanism coupled to the one or more sensory output devices andconfigured for generating a series of programmed stimuli to the patient,and a waveform feature extractor configured to process the EEG data ofthe patient to extract one or ERP responses to the series of programmedstimulus, wherein the program, when executed by one or more processorsof the system, causes the one or more processors to perform the methodsteps of claim 1.